2008 CollaborativeFilteringforImplic

(Hu et al., 2008) ⇒ Yifan Hu, Yehuda Koren, and Chris Volinsky. (2008). “Collaborative Filtering for Implicit Feedback Datasets.” In: Proceedings of the 2008 Eighth IEEE International Conference on Data Mining. ISBN:978-0-7695-3502-9 doi:10.1109/ICDM.2008.22

Subject Headings: Weighted Regularized Matrix Factorization, Alternating Least Squares, Mean Percentage Ranking.

Notes

Cited By

2009

(Rendle et al., 2009) ⇒ Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. (2009). “BPR: Bayesian Personalized Ranking from Implicit Feedback.” In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence. ISBN:978-0-9749039-5-8
- QUOTE: Both Pan et al., 2008 and Hu et al., 2008 have presented a matrix factorization method for item prediction from implicit feedback. Thus the model class is the same as we described in Section 4.3.1, i.e. [math]\displaystyle{ \hat{X} := WH^t }[/math] with the matrices [math]\displaystyle{ W : |U| \times k }[/math] and [math]\displaystyle{ H : |U| \times k }[/math]. The optimization criterion and learning method differ substantially from our approach. Their method is an adaption of a SVD, which minimizes the square-loss. Their extensions are regularization to prevent overfitting and weights in the error function to increase the impact of positive feedback. In total their optimization criterion is: [math]\displaystyle{ \Sigma_{u \in U} \Sigma_{i \in I} C_{ui}( \lt wa-u, h_i\gt - 1)^2 | \lambda || W ||^2_f + \lambda ||H||^2_f }[/math] where [math]\displaystyle{ c_{ui} }[/math] are not model parameters but apriori given weights for each tuple [math]\displaystyle{ (u,i) }[/math]. Hu et al. have additional data to estimate [math]\displaystyle{ c_{ui} for [[positive feedback]] and they set \lt math\gt c_{ui} = 1 }[/math] for the rest.

Quotes

Abstract

A common task of recommender systems is to improve customer experience through personalized recommendations based on prior implicit feedback. These systems passively track different sorts of user behavior, such as purchase history, watching habits and browsing activity, in order to model user preferences. Unlike the much more extensively researched explicit feedback, we do not have any direct input from the users regarding their preferences. In particular, we lack substantial evidence on which products consumer dislike. In this work we identify unique properties of implicit feedback datasets. We propose treating the data as indication of positive and negative preference associated with vastly varying confidence levels. This leads to a factor model which is especially tailored for implicit feedback recommenders. We also suggest a scalable optimization procedure, which scales linearly with the data size. The algorithm is used successfully within a recommender system for television shows. It compares favorably with well tuned implementations of other known methods. In addition, we offer a novel way to give explanations to recommendations given by this factor model.

…

Evaluation methodology

We evaluate a scenario where we generate for each user an ordered list of the shows, sorted from the one predicted to be most preferred till the least preferred one. Then, we present a prefix of the list to the user as the recommended shows. It is important to realize that we do not have a reliable feedback regarding which programs are unloved, as not watching a program can stem from multiple different reasons. In addition, we are currently unable to track user reactions to our recommendations. Thus, precision based metrics are not very appropriate, as they require knowing which programs are undesired to a user. However, watching a program is an indication of liking it, making [[recall-oriented measures applicable.

We denote by rank_{ui} the percentile-ranking of program i within the ordered list of all programs prepared for user u. This way, rankui = 0% would mean that program i is predicted to be the most desirable for user u, thus preceding all other programs in the list. On the other hand, [[rank_{ui}]] = 100% indicates that program i is predicted to be the least preferred for user u, thus placed at the end of the list. (We opted for using percentile-ranks rather than absolute ranks in order to make our discussion general and independent of the number of programs.) Our basic quality measure is the expected percentile ranking of a watching unit in the test period, which is:

[math]\displaystyle{ \bar{\text{rank}} = \frac{\Sigma_{u,i}r^t_{ui} rank_{ui}}{\Sigma_{u,i} r^t_{ui}}. (8) }[/math]

Lower values of [math]\displaystyle{ \bar{\text{rank}} }[/math] are more desirable, as they indicate ranking actually watched shows closer to the top of the recommendation lists. Notice that for random predictions, the expected value of rankui is 50% (placing i in the middle of the sorted list). Thus, rank > 50% indicates an algorithm no better than random.

References

1. Haochao Ying, Liang Chen, Yuwen Xiong, Jian Wu, Collaborative Deep Ranking: A Hybrid Pair-Wise Recommendation Algorithm with Implicit Feedback, Proceedings, Part II, of the 20th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, April 19-22, 2016, Auckland, New Zealand
2. Daryl Lim, Julian McAuley, Gert Lanckriet, Top-N Recommendation with Missing Implicit Feedback, Proceedings of the 9th ACM Conference on Recommender Systems, September 16-20, 2015, Vienna, Austria
3. Yuchen Liu, Dmitry Chechik, Junghoo Cho, Power of Human Curation in Recommendation System, Proceedings of the 25th International Conference Companion on World Wide Web, April 11-15, 2016, Montréal, Québec, Canada
4. Shuang Qiu, Jian Cheng, Ting Yuan, Cong Leng, Hanqing Lu, Item Group based Pairwise Preference Learning for Personalized Ranking, Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval, July 06-11, 2014, Gold Coast, Queensland, Australia
5. Alexandros Karatzoglou, Linas Baltrunas, Yue Shi, Learning to Rank for Recommender Systems, Proceedings of the 7th ACM Conference on Recommender Systems, October 12-16, 2013, Hong Kong, China
6. Xin Wang, Yunhui Guo, Congfu Xu, Recommendation Algorithms for Optimizing Hit Rate, User Satisfaction and Website Revenue, Proceedings of the 24th International Conference on Artificial Intelligence, p.1820-1826, July 25-31, 2015, Buenos Aires, Argentina
7. João Vinagre, Alípio Mário Jorge, João Gama, Collaborative Filtering with Recency-based Negative Feedback, Proceedings of the 30th Annual ACM Symposium on Applied Computing, April 13-17, 2015, Salamanca, Spain
8. Steffen Rendle, Christoph Freudenthaler, Lars Schmidt-Thieme, Factorizing Personalized Markov Chains for Next-basket Recommendation, Proceedings of the 19th International Conference on World Wide Web, April 26-30, 2010, Raleigh, North Carolina, USA
9. István Pilászy, Domonkos Tikk, Computational Complexity Reduction for Factorization-Based Collaborative Filtering Algorithms, Proceedings of the 10th International Conference on E-Commerce and Web Technologies, September 01-04, 2009, Linz, Austria
10. Neil J. Hurley, Personalised Ranking with Diversity, Proceedings of the 7th ACM Conference on Recommender Systems, October 12-16, 2013, Hong Kong, China
11. Sang-Chul Lee, Sang-Wook Kim, Sunju Park, A Graph-Based Recommendation Framework for Price-Comparison Services, Proceedings of the 24th International Conference on World Wide Web, May 18-22, 2015, Florence, Italy
12. Chih-Ming Chen, Ming-Feng Tsai, Yu-Ching Lin, Yi-Hsuan Yang, Query-based Music Recommendations via Preference Embedding, Proceedings of the 10th ACM Conference on Recommender Systems, September 15-19, 2016, Boston, Massachusetts, USA
13. Nathan N. Liu, Evan W. Xiang, Min Zhao, Qiang Yang, Unifying Explicit and Implicit Feedback for Collaborative Filtering, Proceedings of the 19th ACM International Conference on Information and Knowledge Management, October 26-30, 2010, Toronto, ON, Canada
14. Szu-Yu Chou, Yi-Hsuan Yang, Jyh-Shing Roger Jang, Yu-Ching Lin, Addressing Cold Start for Next-song Recommendation, Proceedings of the 10th ACM Conference on Recommender Systems, September 15-19, 2016, Boston, Massachusetts, USA
15. Byoungju Yang, Sangkeun Lee, Sungchan Park, Sang-goo Lee, Exploiting Various Implicit Feedback for Collaborative Filtering, Proceedings of the 21st International Conference Companion on World Wide Web, April 16-20, 2012, Lyon, France
16. Qing Zhang, Houfeng Wang, Improving Collaborative Filtering via Hidden Structured Constraint, Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, October 18-23, 2015, Melbourne, Australia
17. Tural Gurbanov, Francesco Ricci, Meinhard Ploner, Modeling and Predicting User Actions in Recommender Systems, Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization, July 13-17, 2016, Halifax, Nova Scotia, Canada
18. Ernesto Diaz-Aviles, Wolfgang Nejdl, Lucas Drumond, Lars Schmidt-Thieme, Towards Real-time Collaborative Filtering for Big Fast Data, Proceedings of the 22nd International Conference on World Wide Web Companion, May 13-17, 2013, Rio De Janeiro, Brazil
19. Song Xu, Shu Wu, Liang Wang, Personalized Semantic Ranking for Collaborative Recommendation, Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, August 09-13, 2015, Santiago, Chile
20. Fabio Aiolli, Convex AUC Optimization for Top-N Recommendation with Implicit Feedback, Proceedings of the 8th ACM Conference on Recommender Systems, October 06-10, 2014, Foster City, Silicon Valley, California, USA
21. Gábor Takács, István Pilászy, Domonkos Tikk, Applications of the Conjugate Gradient Method for Implicit Feedback Collaborative Filtering, Proceedings of the Fifth ACM Conference on Recommender Systems, October 23-27, 2011, Chicago, Illinois, USA
22. Gábor Takács, Domonkos Tikk, Alternating Least Squares for Personalized Ranking, Proceedings of the Sixth ACM Conference on Recommender Systems, September 09-13, 2012, Dublin, Ireland
23. Yi Fang, Luo Si, Matrix Co-factorization for Recommendation with Rich Side Information and Implicit Feedback, Proceedings of the 2nd International Workshop on Information Heterogeneity and Fusion in Recommender Systems, p.65-69, October 27-27, 2011, Chicago, Illinois
24. Andriy Mnih, Yee Whye Teh, Learning Label Trees for Probabilistic Modelling of Implicit Feedback, Proceedings of the 25th International Conference on Neural Information Processing Systems, p.2816-2824, December 03-06, 2012, Lake Tahoe, Nevada
25. Zhenyue Zhang, Keke Zhao, Hongyuan Zha, Inducible Regularization for Low-rank Matrix Factorizations for Collaborative Filtering, Neurocomputing, 97, p.52-62, November, 2012
26. Anisio Lacerda, Nivio Ziviani, Building User Profiles to Improve User Experience in Recommender Systems, Proceedings of the Sixth ACM International Conference on Web Search and Data Mining, February 04-08, 2013, Rome, Italy
27. Chen Chen, Hongzhi Yin, Junjie Yao, Bin Cui, TeRec: A Temporal Recommender System over Tweet Stream, Proceedings of the VLDB Endowment, v.6 n.12, p.1254-1257, August 2013
28. Bin Wang, Mohammadreza Rahimi, Dequan Zhou, Xin Wang, Expectation-Maximization Collaborative Filtering with Explicit and Implicit Feedback, Proceedings of the 16th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, May 29-June 01, 2012, Kuala Lumpur, Malaysia
29. Mirko Polato, Fabio Aiolli, A Preliminary Study on a Recommender System for the Job Recommendation Challenge, Proceedings of the Recommender Systems Challenge, p.1-4, September 15-15, 2016, Boston, Massachusetts
30. Josef Bauer, Alexandros Nanopoulos, Recommender Systems based on Quantitative Implicit Customer Feedback, Decision Support Systems, v.68 N.C, p.77-88, December 2014
31. Harald Steck, Gaussian Ranking by Matrix Factorization, Proceedings of the 9th ACM Conference on Recommender Systems, September 16-20, 2015, Vienna, Austria
32. Gai Li, Weihua Ou, Pairwise Probabilistic Matrix Factorization for Implicit Feedback Collaborative Filtering, Neurocomputing, v.204 N.C, p.17-25, September 2016
33. Saurabh Kumar, Mayank Kulkarni, Graph based Techniques for User Personalization of News Streams, Proceedings of the 6th ACM India Computing Convention, p.1-7, August 22-25, 2013, Vellore, Tamil Nadu, India
34. Xiwang Yang, Harald Steck, Yang Guo, Yong Liu, On Top-k Recommendation Using Social Networks, Proceedings of the Sixth ACM Conference on Recommender Systems, September 09-13, 2012, Dublin, Ireland
35. Wei Zeng, Li Chen, Heterogeneous Data Fusion via Matrix Factorization for Augmenting Item, Group and Friend Recommendations, Proceedings of the 28th Annual ACM Symposium on Applied Computing, March 18-22, 2013, Coimbra, Portugal
36. Weike Pan, Li Chen, GBPR: Group Preference based Bayesian Personalized Ranking for One-class Collaborative Filtering, Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, August 03-09, 2013, Beijing, China
37. Ilija Ilievski, Sujoy Roy, Personalized News Recommendation based on Implicit Feedback, Proceedings of the 2013 International News Recommender Systems Workshop and Challenge, p.10-15, October 13-13, 2013, Kowloon, Hong Kong
38. Sanjay Purushotham, Yan Liu, C.-C. Jay Kuo, Collaborative Topic Regression with Social Matrix Factorization for Recommendation Systems, Proceedings of the 29th International Coference on International Conference on Machine Learning, p.691-698, June 26-July 01, 2012, Edinburgh, Scotland
39. Quan Yuan, Li Chen, Shiwan Zhao, Factorization Vs. Regularization: Fusing Heterogeneous Social Relationships in Top-n Recommendation, Proceedings of the Fifth ACM Conference on Recommender Systems, October 23-27, 2011, Chicago, Illinois, USA
40. Koen Verstrepen, Bart Goethals, Top-N Recommendation for Shared Accounts, Proceedings of the 9th ACM Conference on Recommender Systems, September 16-20, 2015, Vienna, Austria
41. Emden Gansner, Yifan Hu, Stephen Kobourov, Chris Volinsky, Putting Recommendations on the Map: Visualizing Clusters and Relations, Proceedings of the Third ACM Conference on Recommender Systems, October 23-25, 2009, New York, New York, USA
42. Noriaki Kawamae, Hitoshi Sakano, Takeshi Yamada, Personalized Recommendation based on the Personal Innovator Degree, Proceedings of the Third ACM Conference on Recommender Systems, October 23-25, 2009, New York, New York, USA
43. István Pilászy, Domonkos Tikk, Recommending New Movies: Even a Few Ratings Are More Valuable Than Metadata, Proceedings of the Third ACM Conference on Recommender Systems, October 23-25, 2009, New York, New York, USA
44. Ernesto Diaz-Aviles, Lucas Drumond, Zeno Gantner, Lars Schmidt-Thieme, Wolfgang Nejdl, What is Happening Right Now … That Interests Me?: Online Topic Discovery and Recommendation in Twitter, Proceedings of the 21st ACM International Conference on Information and Knowledge Management, October 29-November 02, 2012, Maui, Hawaii, USA
45. Jian Tang, Jun Yan, Lei Ji, Ming Zhang, Shaodan Guo, Ning Liu, Xianfang Wang, Zheng Chen, Collaborative Users' Brand Preference Mining Across Multiple Domains from Implicit Feedbacks, Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence, p.477-482, August 07-11, 2011, San Francisco, California
46. Zhen Qin, Ish Rishabh, John Carnahan, A Scalable Approach for Periodical Personalized Recommendations, Proceedings of the 10th ACM Conference on Recommender Systems, September 15-19, 2016, Boston, Massachusetts, USA
47. Gawesh Jawaheer, Martin Szomszor, Patty Kostkova, Comparison of Implicit and Explicit Feedback from An Online Music Recommendation Service, Proceedings of the 1st International Workshop on Information Heterogeneity and Fusion in Recommender Systems, p.47-51, September 26-26, 2010, Barcelona, Spain
48. Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, Lars Schmidt-Thieme, BPR: Bayesian Personalized Ranking from Implicit Feedback, Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, p.452-461, June 18-21, 2009, Montreal, Quebec, Canada
49. Ladislav Peska, Peter Vojtas, Negative Implicit Feedback in E-commerce Recommender Systems, Proceedings of the 3rd International Conference on Web Intelligence, Mining and Semantics, June 12-14, 2013, Madrid, Spain
50. Liang Xiang, Quan Yuan, Shiwan Zhao, Li Chen, Xiatian Zhang, Qing Yang, Jimeng Sun, Temporal Recommendation on Graphs via Long- and Short-term Preference Fusion, Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, July 25-28, 2010, Washington, DC, USA
51. Tong Zhao, Julian McAuley, Irwin King, Leveraging Social Connections to Improve Personalized Ranking for Collaborative Filtering, Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, November 03-07, 2014, Shanghai, China
52. Anisio Lacerda, Adriano Veloso, Nivio Ziviani, Exploratory and Interactive Daily Deals Recommendation, Proceedings of the 7th ACM Conference on Recommender Systems, October 12-16, 2013, Hong Kong, China
53. Yu Xin, Harald Steck, Multi-value Probabilistic Matrix Factorization for IP-TV Recommendations, Proceedings of the Fifth ACM Conference on Recommender Systems, October 23-27, 2011, Chicago, Illinois, USA
54. Yi Fang, Luo Si, A Latent Pairwise Preference Learning Approach for Recommendation from Implicit Feedback, Proceedings of the 21st ACM International Conference on Information and Knowledge Management, October 29-November 02, 2012, Maui, Hawaii, USA
55. Tengfei Bao, Yong Ge, Enhong Chen, Hui Xiong, Jilei Tian, Collaborative Filtering with User Ratings and Tags, Proceedings of the 1st International Workshop on Context Discovery and Data Mining, August 12-16, 2012, Beijing, China
56. Noam Koenigstein, Yehuda Koren, Towards Scalable and Accurate Item-oriented Recommendations, Proceedings of the 7th ACM Conference on Recommender Systems, October 12-16, 2013, Hong Kong, China
57. Steffen Rendle, Christoph Freudenthaler, Improving Pairwise Learning for Item Recommendation from Implicit Feedback, Proceedings of the 7th ACM International Conference on Web Search and Data Mining, February 24-28, 2014, New York, New York, USA
58. Xiao Yu, Hao Ma, Bo-June (Paul) Hsu, Jiawei Han, On Building Entity Recommender Systems Using User Click Log and Freebase Knowledge, Proceedings of the 7th ACM International Conference on Web Search and Data Mining, February 24-28, 2014, New York, New York, USA
59. Ignacio Fernández-Tobías, Paolo Tomeo, Iván Cantador, Tommaso Di Noia, Eugenio Di Sciascio, Accuracy and Diversity in Cross-domain Recommendations for Cold-start Users with Positive-only Feedback, Proceedings of the 10th ACM Conference on Recommender Systems, September 15-19, 2016, Boston, Massachusetts, USA
60. Josef Bauer, Alexandros Nanopoulos, A Framework for Matrix Factorization based on General Distributions, Proceedings of the 8th ACM Conference on Recommender Systems, October 06-10, 2014, Foster City, Silicon Valley, California, USA
61. Balázs Hidasi, Domonkos Tikk, Enhancing Matrix Factorization through Initialization for Implicit Feedback Databases, Proceedings of the 2nd Workshop on Context-awareness in Retrieval and Recommendation, p.2-9, February 14-14, 2012, Lisbon, Portugal
62. Liang Hu, Jian Cao, Guandong Xu, Longbing Cao, Zhiping Gu, Can Zhu, Personalized Recommendation via Cross-domain Triadic Factorization, Proceedings of the 22nd International Conference on World Wide Web, May 13-17, 2013, Rio De Janeiro, Brazil
63. Harald Steck, Training and Testing of Recommender Systems on Data Missing Not at Random, Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, July 25-28, 2010, Washington, DC, USA
64. Edward Rolando Núñez-Valdéz, Juan Manuel Cueva Lovelle, Oscar Sanjuán Martínez, Vicente García-Díaz, Patricia Ordoñez De Pablos, Carlos Enrique Montenegro Marín, Implicit Feedback Techniques on Recommender Systems Applied to Electronic Books, Computers in Human Behavior, v.28 n.4, p.1186-1193, July, 2012
65. Noriaki Kawamae, Predicting Future Reviews: Sentiment Analysis Models for Collaborative Filtering, Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, February 09-12, 2011, Hong Kong, China
66. Haolan Chen, Di Niu, Kunfeng Lai, Yu Xu, Masoud Ardakani, Separating-Plane Factorization Models: Scalable Recommendation from One-Class Implicit Feedback, Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, October 24-28, 2016, Indianapolis, Indiana, USA
67. Yue Shi, Alexandros Karatzoglou, Linas Baltrunas, Martha Larson, Nuria Oliver, Alan Hanjalic, CLiMF: Collaborative Less-is-more Filtering, Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, August 03-09, 2013, Beijing, China
68. Ladislav Peška, Peter Vojtáš, Estimating Importance of Implicit Factors in E-commerce Recommender Systems, Proceedings of the 2nd International Conference on Web Intelligence, Mining and Semantics, June 13-15, 2012, Craiova, Romania
69. Yao Wu, Christopher DuBois, Alice X. Zheng, Martin Ester, Collaborative Denoising Auto-Encoders for Top-N Recommender Systems, Proceedings of the Ninth ACM International Conference on Web Search and Data Mining, February 22-25, 2016, San Francisco, California, USA
70. Steffen Rendle, Lars Schmidt-Thieme, Pairwise Interaction Tensor Factorization for Personalized Tag Recommendation, Proceedings of the Third ACM International Conference on Web Search and Data Mining, February 04-06, 2010, New York, New York, USA
71. Noraswaliza Abdullah, Yue Xu, Shlomo Geva, Integrating Collaborative Filtering and Matching-based Search for Product Recommendations, Journal of Theoretical and Applied Electronic Commerce Research, v.8 n.2, p.34-48, August 2013
72. Guang Ling, Haiqin Yang, Michael R. Lyu, Irwin King, Response Aware Model-based Collaborative Filtering, Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence, August 14-18, 2012, Catalina Island, CA
73. Sheng Wang, Fangtao Li, Ming Zhang, Supervised Topic Model with Consideration of User and Item, Proceedings of the 17th AAAI Conference on Late-Breaking Developments in the Field of Artificial Intelligence, p.137-139, January 01, 2013
74. Huayu Li, Yong Ge, Richang Hong, Hengshu Zhu, Point-of-Interest Recommendations: Learning Potential Check-ins from Friends, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 13-17, 2016, San Francisco, California, USA
75. Emile Richard, Nicolas Baskiotis, Theodoros Evgeniou, Nicolas Vayatis, Link Discovery Using Graph Feature Tracking, Proceedings of the 23rd International Conference on Neural Information Processing Systems, p.1966-1974, December 06-09, 2010, Vancouver, British Columbia, Canada
76. Katja Hofmann, Anne Schuth, Alejandro Bellogín, Maarten Rijke, Effects of Position Bias on Click-Based Recommender Evaluation, Proceedings of the 36th European Conference on IR Research on Advances in Information Retrieval, April 13-16, 2014, Amsterdam, The Netherlands
77. Katja Hofmann, Anne Schuth, Alejandro Bellogín, Maarten Rijke, Effects of Position Bias on Click-Based Recommender Evaluation, Proceedings of the 36th European Conference on IR Research on Advances in Information Retrieval, April 13-16, 2014, Amsterdam, The Netherlands
78. Mona Nasery, Matthias Braunhofer, Francesco Ricci, Recommendations with Optimal Combination of Feature-Based and Item-Based Preferences, Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization, July 13-17, 2016, Halifax, Nova Scotia, Canada
79. Suhrid Balakrishnan, Rensheng Wang, Carlos Scheidegger, Angus MacLellan, Yifan Hu, Aaron Archer, Shankar Krishnan, David Applegate, Guang Qin Ma, S. Tom Au, Combining Predictors for Recommending Music: The False Positives' Approach to KDD Cup Track 2, Proceedings of the 2011 International Conference on KDD Cup 2011, January 01, 2011
80. Andriy Mnih, Taxonomy-informed Latent Factor Models for Implicit Feedback, Proceedings of the 2011 International Conference on KDD Cup 2011, January 01, 2011
81. Gideon Dror, Noam Koenigstein, Yehuda Koren, Markus Weimer, The Yahoo! Music Dataset and KDD-Cup'11, Proceedings of the 2011 International Conference on KDD Cup 2011, January 01, 2011
82. Xing Yi, Liangjie Hong, Erheng Zhong, Nanthan Nan Liu, Suju Rajan, Beyond Clicks: Dwell Time for Personalization, Proceedings of the 8th ACM Conference on Recommender Systems, October 06-10, 2014, Foster City, Silicon Valley, California, USA
83. Rob Hall, Josh Attenberg, Fast and Accurate Maximum Inner Product Recommendations on Map-Reduce, Proceedings of the 24th International Conference on World Wide Web, May 18-22, 2015, Florence, Italy
84. Suhang Wang, Jiliang Tang, Huan Liu, Toward Dual Roles of Users in Recommender Systems, Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, October 18-23, 2015, Melbourne, Australia
85. Weike Pan, Evan W. Xiang, Nathan N. Liu, Qiang Yang, Transfer Learning in Collaborative Filtering for Sparsity Reduction, Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence, p.230-235, July 11-15, 2010, Atlanta, Georgia
86. Tong Zhao, Julian McAuley, Irwin King, Improving Latent Factor Models via Personalized Feature Projection for One Class Recommendation, Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, October 18-23, 2015, Melbourne, Australia
87. Zeno Gantner, Lucas Drumond, Christoph Freudenthaler, Lars Schmidt-Thieme, Personalized Ranking for Non-uniformly Sampled Items, Proceedings of the 2011 International Conference on KDD Cup 2011, January 01, 2011
88. Natalie Aizenberg, Yehuda Koren, Oren Somekh, Build Your Own Music Recommender by Modeling Internet Radio Streams, Proceedings of the 21st International Conference on World Wide Web, April 16-20, 2012, Lyon, France
89. Ziwon Hyung, Kibeom Lee, Kyogu Lee, Music Recommendation Using Text Analysis on Song Requests to Radio Stations, Expert Systems with Applications: An International Journal, v.41 n.5, p.2608-2618, April, 2014
90. Jean-Benoit Griesner, Talel Abdessalem, Hubert Naacke, POI Recommendation: Towards Fused Matrix Factorization with Geographical and Temporal Influences, Proceedings of the 9th ACM Conference on Recommender Systems, September 16-20, 2015, Vienna, Austria
91. Alexandros Karatzoglou, Linas Baltrunas, Karen Church, Matthias Böhmer, Climbing the App Wall: Enabling Mobile App Discovery through Context-aware Recommendations, Proceedings of the 21st ACM International Conference on Information and Knowledge Management, October 29-November 02, 2012, Maui, Hawaii, USA
92. Ulrich Paquet, Noam Koenigstein, One-class Collaborative Filtering with Random Graphs, Proceedings of the 22nd International Conference on World Wide Web, May 13-17, 2013, Rio De Janeiro, Brazil
93. Yin Zheng, Cailiang Liu, Bangsheng Tang, Hanning Zhou, Neural Autoregressive Collaborative Filtering for Implicit Feedback, Proceedings of the 1st Workshop on Deep Learning for Recommender Systems, p.2-6, September 15-15, 2016, Boston, MA, USA
94. Nathan N. Liu, Luheng He, Min Zhao, Social Temporal Collaborative Ranking for Context Aware Movie Recommendation, ACM Transactions on Intelligent Systems and Technology (TIST), v.4 n.1, January 2013
95. Makoto Nakatsuji, Yasuhiro Fujiwara, Toshio Uchiyama, Hiroyuki Toda, Collaborative Filtering by Analyzing Dynamic User Interests Modeled by Taxonomy, Proceedings of the 11th International Conference on The Semantic Web, November 11-15, 2012, Boston, MA
96. Sanjay Purushotham, C.-C. Jay Kuo, Junaith Shahabdeen, Lama Nachman, Collaborative Group-activity Recommendation in Location-based Social Networks, Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Crowdsourced and Volunteered Geographic Information, p.8-15, November 04-04, 2014, Dallas, Texas
97. Jianping Shi, Naiyan Wang, Yang Xia, Dit-Yan Yeung, Irwin King, Jiaya Jia, SCMF: Sparse Covariance Matrix Factorization for Collaborative Filtering, Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, August 03-09, 2013, Beijing, China
98. Qiuxia Lu, Diyi Yang, Tianqi Chen, Weinan Zhang, Yong Yu, Informative Household Recommendation with Feature-based Matrix Factorization, Proceedings of the 2nd Challenge on Context-Aware Movie Recommendation, p.15-22, October 23-27, 2011, Chicago, Illinois
99. Jeroen B. P. Vuurens, Martha Larson, Arjen P. De Vries, Exploring Deep Space: Learning Personalized Ranking in a Semantic Space, Proceedings of the 1st Workshop on Deep Learning for Recommender Systems, p.23-28, September 15-15, 2016, Boston, MA, USA
100. Xia Ning, George Karypis, Sparse Linear Methods with Side Information for Top-n Recommendations, Proceedings of the Sixth ACM Conference on Recommender Systems, September 09-13, 2012, Dublin, Ireland
101. István Pilászy, Dávid Zibriczky, Domonkos Tikk, Fast Als-based Matrix Factorization for Explicit and Implicit Feedback Datasets, Proceedings of the Fourth ACM Conference on Recommender Systems, September 26-30, 2010, Barcelona, Spain
102. Sangkeun Lee, Sang-il Song, Minsuk Kahng, Dongjoo Lee, Sang-goo Lee, Random Walk based Entity Ranking on Graph for Multidimensional Recommendation, Proceedings of the Fifth ACM Conference on Recommender Systems, October 23-27, 2011, Chicago, Illinois, USA
103. Marko Gasparic, Context-Based IDE Command Recommender System, Proceedings of the 10th ACM Conference on Recommender Systems, September 15-19, 2016, Boston, Massachusetts, USA
104. Fabio Aiolli, Efficient Top-n Recommendation for Very Large Scale Binary Rated Datasets, Proceedings of the 7th ACM Conference on Recommender Systems, October 12-16, 2013, Hong Kong, China
105. Gang Zhao, Mong LI Lee, Hsu Wynne, Utilizing Purchase Intervals in Latent Clusters for Product Recommendation, Proceedings of the 8th Workshop on Social Network Mining and Analysis, p.1-9, August 24-27, 2014, New York, NY, USA
106. Jia Chen, Qin Jin, Shiwan Zhao, Shenghua Bao, Li Zhang, Zhong Su, Yong Yu, Does Product Recommendation Meet Its Waterloo in Unexplored Categories?: No, Price Comes to Help, Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval, July 06-11, 2014, Gold Coast, Queensland, Australia
107. Koen Verstrepen, Bart Goethals, Unifying Nearest Neighbors Collaborative Filtering, Proceedings of the 8th ACM Conference on Recommender Systems, October 06-10, 2014, Foster City, Silicon Valley, California, USA
108. Guang Ling, Michael R. Lyu, Irwin King, Ratings Meet Reviews, a Combined Approach to Recommend, Proceedings of the 8th ACM Conference on Recommender Systems, October 06-10, 2014, Foster City, Silicon Valley, California, USA
109. Yang Bao, Hui Fang, Jie Zhang, TopicMF: Simultaneously Exploiting Ratings and Reviews for Recommendation, Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, p.2-8, July 27-31, 2014, Québec City, Québec, Canada
110. Julien Delporte, Alexandros Karatzoglou, Tomasz Matuszczyk, Stéphane Canu, Socially Enabled Preference Learning from Implicit Feedback Data, Proceedings, Part II, of the European Conference on Machine Learning and Knowledge Discovery in Databases, September 23-27, 2013, Prague, Czech Republic
111. Yong Liu, Wei Wei, Aixin Sun, Chunyan Miao, Exploiting Geographical Neighborhood Characteristics for Location Recommendation, Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, November 03-07, 2014, Shanghai, China
112. Rohit Parimi, Doina Caragea, Cross-Domain Matrix Factorization for Multiple Implicit-Feedback Domains, Revised Selected Papers of the First International Workshop on Machine Learning, Optimization, and Big Data, p.80-92, July 21-23, 2015
113. Ruining He, Julian McAuley, VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback, Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, February 12-17, 2016, Phoenix, Arizona
114. Defu Lian, Cong Zhao, Xing Xie, Guangzhong Sun, Enhong Chen, Yong Rui, GeoMF: Joint Geographical Modeling and Matrix Factorization for Point-of-interest Recommendation, Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 24-27, 2014, New York, New York, USA
115. Cho-Jui Hsieh, Nagarajan Natarajan, Inderjit S Dhillon, PU Learning for Matrix Completion, Proceedings of the 32nd International Conference on International Conference on Machine Learning, July 06-11, 2015, Lille, France
116. Chaochao Chen, Xiaolin Zheng, Yan Wang, Fuxing Hong, Zhen Lin, Context- Ware Collaborative Topic Regression with Social Matrix Factorization for Recommender Systems, Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, p.9-15, July 27-31, 2014, Québec City, Québec, Canada
117. Weilong Yao, Jing He, Hua Wang, Yanchun Zhang, Jie Cao, Collaborative Topic Ranking: Leveraging Item Meta-data for Sparsity Reduction, Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, p.374-380, January 25-30, 2015, Austin, Texas
118. Nesrine Mezhoudi, User Interface Adaptation based on User Feedback and Machine Learning, Proceedings of the Companion Publication of the 2013 International Conference on Intelligent User Interfaces Companion, March 19-22, 2013, Santa Monica, California, USA
119. Ernesto Diaz-Aviles, Lucas Drumond, Lars Schmidt-Thieme, Wolfgang Nejdl, Real-time Top-n Recommendation in Social Streams, Proceedings of the Sixth ACM Conference on Recommender Systems, September 09-13, 2012, Dublin, Ireland
120. Hansu Gu, Mike Gartrell, Liang Zhang, Qin Lv, Dirk Grunwald, AnchorMF: Towards Effective Event Context Identification, Proceedings of the 22nd ACM International Conference on Conference on Information & Knowledge Management, October 27-November 01, 2013, San Francisco, California, USA
121. Marcelo Garcia Manzato, GSVD++: Supporting Implicit Feedback on Recommender Systems with Metadata Awareness, Proceedings of the 28th Annual ACM Symposium on Applied Computing, March 18-22, 2013, Coimbra, Portugal
122. Pei Lee, Laks V.S. Lakshmanan, Mitul Tiwari, Sam Shah, Modeling Impression Discounting in Large-scale Recommender Systems, Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 24-27, 2014, New York, New York, USA
123. Wenxing Hong, Lei Li, Tao Li, Wenfu Pan, IHR: An Online Recruiting System for Xiamen Talent Service Center, Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 11-14, 2013, Chicago, Illinois, USA
124. Sujoy Roy, Sharath Chandra Guntuku, Latent Factor Representations for Cold-Start Video Recommendation, Proceedings of the 10th ACM Conference on Recommender Systems, September 15-19, 2016, Boston, Massachusetts, USA
125. Omar Moling, Linas Baltrunas, Francesco Ricci, Optimal Radio Channel Recommendations with Explicit and Implicit Feedback, Proceedings of the Sixth ACM Conference on Recommender Systems, September 09-13, 2012, Dublin, Ireland
126. Harald Steck, Evaluation of Recommendations: Rating-prediction and Ranking, Proceedings of the 7th ACM Conference on Recommender Systems, October 12-16, 2013, Hong Kong, China
127. Noam Koenigstein, Ulrich Paquet, Xbox Movies Recommendations: Variational Bayes Matrix Factorization with Embedded Feature Selection, Proceedings of the 7th ACM Conference on Recommender Systems, October 12-16, 2013, Hong Kong, China
128. Trung V. Nguyen, Alexandros Karatzoglou, Linas Baltrunas, Gaussian Process Factorization Machines for Context-aware Recommendations, Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval, July 06-11, 2014, Gold Coast, Queensland, Australia
129. Dawen Liang, Laurent Charlin, James McInerney, David M. Blei, Modeling User Exposure in Recommendation, Proceedings of the 25th International Conference on World Wide Web, April 11-15, 2016, Montréal, Québec, Canada
130. Suvash Sedhain, Aditya Krishna Menon, Scott Sanner, Darius Braziunas, On the Effectiveness of Linear Models for One-class Collaborative Filtering, Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, February 12-17, 2016, Phoenix, Arizona
131. Saúl Vargas, Pablo Castells, Improving Sales Diversity by Recommending Users to Items, Proceedings of the 8th ACM Conference on Recommender Systems, October 06-10, 2014, Foster City, Silicon Valley, California, USA
132. Dohyung Park, Joe Neeman, Jin Zhang, Sujay Sanghavi, Inderjit S. Dhillon, Preference Completion: Large-scale Collaborative Ranking from Pairwise Comparisons, Proceedings of the 32nd International Conference on International Conference on Machine Learning, July 06-11, 2015, Lille, France
133. Yueyang Wang, Yuanfang Xia, Siliang Tang, Fei Wu, Yueting Zhuang, Flickr Group Recommendation via Heterogeneous Information Networks, Proceedings of the 7th International Conference on Internet Multimedia Computing and Service, p.1-5, August 19-21, 2015, Zhangjiajie, Hunan, China
134. Oren Sar Shalom, Noam Koenigstein, Ulrich Paquet, Hastagiri P. Vanchinathan, Beyond Collaborative Filtering: The List Recommendation Problem, Proceedings of the 25th International Conference on World Wide Web, April 11-15, 2016, Montréal, Québec, Canada
135. Beyza Ermis, Guillaume Bouchard, Iterative Splits of Quadratic Bounds for Scalable Binary Tensor Factorization, Proceedings of the Thirtieth Conference on Uncertainty in Artificial Intelligence, July 23-27, 2014, Quebec City, Quebec, Canada
136. Xin Li, Guandong Xu, Enhong Chen, Yu Zong, Learning Recency based Comparative Choice Towards Point-of-interest Recommendation, Expert Systems with Applications: An International Journal, v.42 n.9, p.4274-4283, June 2015
137. Charanpal Dhanjal, Stéphan Clémençon, Romaric Gaudel, Collaborative Filtering with Localised Ranking, Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, p.2554-2560, January 25-30, 2015, Austin, Texas
138. Masahiro Sato, Hidetaka Izumo, Takashi Sonoda, Modeling Individual Users' Responsiveness to Maximize Recommendation Impact, Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization, July 13-17, 2016, Halifax, Nova Scotia, Canada
139. Jinoh Oh, Wook-Shin Han, Hwanjo Yu, Xiaoqian Jiang, Fast and Robust Parallel SGD Matrix Factorization, Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 10-13, 2015, Sydney, NSW, Australia
140. Nathan N. Liu, Bin Cao, Min Zhao, Qiang Yang, Adapting Neighborhood and Matrix Factorization Models for Context Aware Recommendation, Proceedings of the Workshop on Context-Aware Movie Recommendation, p.7-13, September 30-30, 2010, Barcelona, Spain
141. Chunfeng Yang, Yipeng Zhou, Liang Chen, Xiaopeng Zhang, Dah Ming Chiu, Social Group Based Video Recommendation Addressing the Cold-Start Problem, Proceedings, Part II, of the 20th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, April 19-22, 2016, Auckland, New Zealand
142. Yong Liu, Peilin Zhao, Aixin Sun, Chunyan Miao, A Boosting Algorithm for Item Recommendation with Implicit Feedback, Proceedings of the 24th International Conference on Artificial Intelligence, p.1792-1798, July 25-31, 2015, Buenos Aires, Argentina
143. Sven Strickroth, Niels Pinkwart, High Quality Recommendations for Small Communities: The Case of a Regional Parent Network, Proceedings of the Sixth ACM Conference on Recommender Systems, September 09-13, 2012, Dublin, Ireland
144. Paolo Cremonesi, Yehuda Koren, Roberto Turrin, Performance of Recommender Algorithms on Top-n Recommendation Tasks, Proceedings of the Fourth ACM Conference on Recommender Systems, September 26-30, 2010, Barcelona, Spain
145. Yanen Li, Jia Hu, ChengXiang Zhai, Ye Chen, Improving One-class Collaborative Filtering by Incorporating Rich User Information, Proceedings of the 19th ACM International Conference on Information and Knowledge Management, October 26-30, 2010, Toronto, ON, Canada
146. Donghyun Kim, Chanyoung Park, Jinoh Oh, Sungyoung Lee, Hwanjo Yu, Convolutional Matrix Factorization for Document Context-Aware Recommendation, Proceedings of the 10th ACM Conference on Recommender Systems, September 15-19, 2016, Boston, Massachusetts, USA
147. Denis Parra, Xavier Amatriain, Walk the Talk: Analyzing the Relation Between Implicit and Explicit Feedback for Preference Elicitation, Proceedings of the 19th International Conference on User Modeling, Adaption, and Personalization, July 11-15, 2011, Girona, Spain
148. Xiao Yu, Xiang Ren, Yizhou Sun, Quanquan Gu, Bradley Sturt, Urvashi Khandelwal, Brandon Norick, Jiawei Han, Personalized Entity Recommendation: A Heterogeneous Information Network Approach, Proceedings of the 7th ACM International Conference on Web Search and Data Mining, February 24-28, 2014, New York, New York, USA
149. Dawen Liang, Jaan Altosaar, Laurent Charlin, David M. Blei, Factorization Meets the Item Embedding: Regularizing Matrix Factorization with Item Co-occurrence, Proceedings of the 10th ACM Conference on Recommender Systems, September 15-19, 2016, Boston, Massachusetts, USA
150. Shenglin Zhao, Tong Zhao, Haiqin Yang, Michael R. Lyu, Irwin King, STELLAR: Spatial-temporal Latent Ranking for Successive Point-of-interest Recommendation, Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, February 12-17, 2016, Phoenix, Arizona
151. Yin Zheng, Bangsheng Tang, Wenkui Ding, Hanning Zhou, A Neural Autoregressive Approach to Collaborative Filtering, Proceedings of the 33rd International Conference on International Conference on Machine Learning, June 19-24, 2016, New York, NY, USA
152. Evangelia Christakopoulou, Moving Beyond Linearity and Independence in Top-N Recommender Systems, Proceedings of the 8th ACM Conference on Recommender Systems, October 06-10, 2014, Foster City, Silicon Valley, California, USA
153. Jérémie Rappaz, Maria-Luiza Vladarean, Julian McAuley, Michele Catasta, Bartering Books to Beers: A Recommender System for Exchange Platforms, Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, February 06-10, 2017, Cambridge, United Kingdom
154. Zhao Kang, Chong Peng, Qiang Cheng, Top-N Recommender System via Matrix Completion, Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, February 12-17, 2016, Phoenix, Arizona
155. Robin Devooght, Nicolas Kourtellis, Amin Mantrach, Dynamic Matrix Factorization with Priors on Unknown Values, Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 10-13, 2015, Sydney, NSW, Australia
156. Pengfei Wang, Jiafeng Guo, Yanyan Lan, Jun Xu, Shengxian Wan, Xueqi Cheng, Learning Hierarchical Representation Model for NextBasket Recommendation, Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, August 09-13, 2015, Santiago, Chile
157. Liang Hu, Jian Cao, Guandong Xu, Longbing Cao, Zhiping Gu, Wei Cao, Deep Modeling of Group Preferences for Group-based Recommendation, Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, p.1861-1867, July 27-31, 2014, Québec City, Québec, Canada
158. Xiwang Yang, Yang Guo, Yong Liu, Harald Steck, A Survey of Collaborative Filtering based Social Recommender Systems, Computer Communications, 41, p.1-10, March, 2014
159. Bruno Pradel, Savaneary Sean, Julien Delporte, Sébastien Guérif, Céline Rouveirol, Nicolas Usunier, Françoise Fogelman-Soulié, Frédéric Dufau-Joel, A Case Study in a Recommender System based on Purchase Data, Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 21-24, 2011, San Diego, California, USA
160. Zhao Kang, Chong Peng, Ming Yang, Qiang Cheng, Top-N Recommendation on Graphs, Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, October 24-28, 2016, Indianapolis, Indiana, USA
161. Xuetao Ding, Xiaoming Jin, Yujia Li, Lianghao Li, Celebrity Recommendation with Collaborative Social Topic Regression, Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, August 03-09, 2013, Beijing, China
162. Yuan Cao Zhang, Diarmuid Ó Séaghdha, Daniele Quercia, Tamas Jambor, Auralist: Introducing Serendipity Into Music Recommendation, Proceedings of the Fifth ACM International Conference on Web Search and Data Mining, February 08-12, 2012, Seattle, Washington, USA
163. Roy Levin, Hassan Abassi, Uzi Cohen, Guided Walk: A Scalable Recommendation Algorithm for Complex Heterogeneous Social Networks, Proceedings of the 10th ACM Conference on Recommender Systems, September 15-19, 2016, Boston, Massachusetts, USA
164. Sangkeun Lee, Minsuk Kahng, Sang-goo Lee, Flexible Recommendation Using Random Walks on Implicit Feedback Graph, Proceedings of the 5th International Conference on Ubiquitous Information Management and Communication, February 21-23, 2011, Seoul, Korea
165. Zeno Gantner, Steffen Rendle, Christoph Freudenthaler, Lars Schmidt-Thieme, MyMediaLite: A Free Recommender System Library, Proceedings of the Fifth ACM Conference on Recommender Systems, October 23-27, 2011, Chicago, Illinois, USA
166. Diyi Yang, Tianqi Chen, Weinan Zhang, Qiuxia Lu, Yong Yu, Local Implicit Feedback Mining for Music Recommendation, Proceedings of the Sixth ACM Conference on Recommender Systems, September 09-13, 2012, Dublin, Ireland
167. Sindhu Raghavan, Suriya Gunasekar, Joydeep Ghosh, Review Quality Aware Collaborative Filtering, Proceedings of the Sixth ACM Conference on Recommender Systems, September 09-13, 2012, Dublin, Ireland
168. Harald Steck, Item Popularity and Recommendation Accuracy, Proceedings of the Fifth ACM Conference on Recommender Systems, October 23-27, 2011, Chicago, Illinois, USA
169. Diego Saez-Trumper, Daniele Quercia, Jon Crowcroft, Ads and the City: Considering Geographic Distance Goes a Long Way, Proceedings of the Sixth ACM Conference on Recommender Systems, September 09-13, 2012, Dublin, Ireland
170. Yang Song, Ali Mamdouh Elkahky, Xiaodong He, Multi-Rate Deep Learning for Temporal Recommendation, Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, July 17-21, 2016, Pisa, Italy
171. Paolo Tomeo, Ignacio Fernández-Tobías, Tommaso Di Noia, Iván Cantador, Exploiting Linked Open Data in Cold-start Recommendations with Positive-only Feedback, Proceedings of the 4th Spanish Conference on Information Retrieval, June 14-16, 2016, Granada, Spain
172. Weiyu Guo, Shu Wu, Liang Wang, Tieniu Tan, Personalized Ranking with Pairwise Factorization Machines, Neurocomputing, v.214 N.C, p.191-200, November 2016
173. Hao Wang, Xingjian Shi, Dit-Yan Yeung, Relational Stacked Denoising Autoencoder for Tag Recommendation, Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, p.3052-3058, January 25-30, 2015, Austin, Texas
174. Aditya Krishna Menon, Krishna-Prasad Chitrapura, Sachin Garg, Deepak Agarwal, Nagaraj Kota, Response Prediction Using Collaborative Filtering with Hierarchies and Side-information, Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 21-24, 2011, San Diego, California, USA
175. Xiang Ren, Jialu Liu, Xiao Yu, Urvashi Khandelwal, Quanquan Gu, Lidan Wang, Jiawei Han, ClusCite: Effective Citation Recommendation by Information Network-based Clustering, Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 24-27, 2014, New York, New York, USA
176. Xin Xin, Zhirun Liu, Chin-Yew Lin, Heyan Huang, Xiaochi Wei, Ping Guo, Cross-domain Collaborative Filtering with Review Text, Proceedings of the 24th International Conference on Artificial Intelligence, p.1827-1833, July 25-31, 2015, Buenos Aires, Argentina
177. Saúl Vargas, Linas Baltrunas, Alexandros Karatzoglou, Pablo Castells, Coverage, Redundancy and Size-awareness in Genre Diversity for Recommender Systems, Proceedings of the 8th ACM Conference on Recommender Systems, October 06-10, 2014, Foster City, Silicon Valley, California, USA
178. Stéphane Caron, Smriti Bhagat, Mixing Bandits: A Recipe for Improved Cold-start Recommendations in a Social Network, Proceedings of the 7th Workshop on Social Network Mining and Analysis, p.1-9, August 11-11, 2013, Chicago, Illinois
179. Nathan N. Liu, Min Zhao, Evan Xiang, Qiang Yang, Online Evolutionary Collaborative Filtering, Proceedings of the Fourth ACM Conference on Recommender Systems, September 26-30, 2010, Barcelona, Spain
180. Ramon Lopes, Renato Assunção, Rodrygo L.T. Santos, Efficient Bayesian Methods for Graph-based Recommendation, Proceedings of the 10th ACM Conference on Recommender Systems, September 15-19, 2016, Boston, Massachusetts, USA
181. Diane J. Hu, Rob Hall, Josh Attenberg, Style in the Long Tail: Discovering Unique Interests with Latent Variable Models in Large Scale Social E-commerce, Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 24-27, 2014, New York, New York, USA
182. Lei Li, Tao Li, MEET: A Generalized Framework for Reciprocal Recommender Systems, Proceedings of the 21st ACM International Conference on Information and Knowledge Management, October 29-November 02, 2012, Maui, Hawaii, USA
183. Bin Liu, Yanjie Fu, Zijun Yao, Hui Xiong, Learning Geographical Preferences for Point-of-interest Recommendation, Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 11-14, 2013, Chicago, Illinois, USA
184. Chen Chen, Hanghang Tong, Lei Xie, Lei Ying, Qing He, FASCINATE: Fast Cross-Layer Dependency Inference on Multi-layered Networks, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 13-17, 2016, San Francisco, California, USA
185. Yuan Yao, Hanghang Tong, Guo Yan, Feng Xu, Xiang Zhang, Boleslaw K. Szymanski, Jian Lu, Dual-Regularized One-Class Collaborative Filtering, Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, November 03-07, 2014, Shanghai, China
186. Marcelo G. Manzato, Marcos A. Domingues, Solange O. Rezende, Optimizing Personalized Ranking in Recommender Systems with Metadata Awareness, Proceedings of the 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT), p.191-197, August 11-14, 2014
187. Christopher H. Lin, Ece Kamar, Eric Horvitz, Signals in the Silence: Models of Implicit Feedback in a Recommendation System for Crowdsourcing, Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, p.908-914, July 27-31, 2014, Québec City, Québec, Canada
188. Faris Alqadah, Chandan K. Reddy, Junling Hu, Hatim F. Alqadah, Biclustering Neighborhood-based Collaborative Filtering Method for Top-n Recommender Systems, Knowledge and Information Systems, v.44 n.2, p.475-491, August 2015
189. Mingyue Zhang, Xunhua Guo, Guoqing Chen, Prediction Uncertainty in Collaborative Filtering, Decision Support Systems, v.83 N.C, p.10-21, March 2016
190. Xiangnan He, Hanwang Zhang, Min-Yen Kan, Tat-Seng Chua, Fast Matrix Factorization for Online Recommendation with Implicit Feedback, Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, July 17-21, 2016, Pisa, Italy
191. Balázs Hidasi, Domonkos Tikk, Speeding Up ALS Learning via Approximate Methods for Context-aware Recommendations, Knowledge and Information Systems, v.47 n.1, p.131-155, April 2016
192. Yanxiang Huang, Bin Cui, Wenyu Zhang, Jie Jiang, Ying Xu, TencentRec: Real-time Stream Recommendation in Practice, Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, May 31-June 04, 2015, Melbourne, Victoria, Australia
193. Andreu Vall, Listener-Inspired Automated Music Playlist Generation, Proceedings of the 9th ACM Conference on Recommender Systems, September 16-20, 2015, Vienna, Austria
194. Jia Chen, Qin Jin, Shiwan Zhao, Shenghua Bao, Li Zhang, Zhong Su, Yong Yu, Boosting Recommendation in Unexplored Categories by User Price Preference, ACM Transactions on Information Systems (TOIS), v.35 n.2, p.1-27, October 2016
195. Yue Shi, Alexandros Karatzoglou, Linas Baltrunas, Martha Larson, Nuria Oliver, Alan Hanjalic, CLiMF: Learning to Maximize Reciprocal Rank with Collaborative Less-is-more Filtering, Proceedings of the Sixth ACM Conference on Recommender Systems, September 09-13, 2012, Dublin, Ireland
196. Chong Wang, David M. Blei, Collaborative Topic Modeling for Recommending Scientific Articles, Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 21-24, 2011, San Diego, California, USA
197. Sebastian Schelter, Christoph Boden, Martin Schenck, Alexander Alexandrov, Volker Markl, Distributed Matrix Factorization with Mapreduce Using a Series of Broadcast-joins, Proceedings of the 7th ACM Conference on Recommender Systems, October 12-16, 2013, Hong Kong, China
198. Yidan Liu, Min Xie, Laks V.S. Lakshmanan, Recommending User Generated Item Lists, Proceedings of the 8th ACM Conference on Recommender Systems, October 06-10, 2014, Foster City, Silicon Valley, California, USA
199. Haijun Zhang, Zhoujun Li, Yan Chen, Xiaoming Zhang, Senzhang Wang, Exploit Latent Dirichlet Allocation for One-Class Collaborative Filtering, Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, November 03-07, 2014, Shanghai, China
200. Prem Gopalan, Laurent Charlin, David M. Blei, Content-based Recommendations with Poisson Factorization, Proceedings of the 27th International Conference on Neural Information Processing Systems, p.3176-3184, December 08-13, 2014, Montreal, Canada
201. Liangyue Li, Yuan Yao, Jie Tang, Wei Fan, Hanghang Tong, QUINT: On Query-Specific Optimal Networks, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 13-17, 2016, San Francisco, California, USA
202. Shuguang Han, Peng Dai, Praveen Paritosh, David Huynh, Crowdsourcing Human Annotation on Web Page Structure: Infrastructure Design and Behavior-Based Quality Control, ACM Transactions on Intelligent Systems and Technology (TIST), v.7 n.4, May 2016
203. Beidou Wang, Martin Ester, Yikang Liao, Jiajun Bu, Yu Zhu, Ziyu Guan, Deng Cai, The Million Domain Challenge: Broadcast Email Prioritization by Cross-domain Recommendation, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 13-17, 2016, San Francisco, California, USA
204. Xiang Li, Charles X. Ling, Huaimin Wang, The Convergence Behavior of Naive Bayes on Large Sparse Datasets, ACM Transactions on Knowledge Discovery from Data (TKDD), v.11 n.1, p.1-24, August 2016
205. Yan-Fu Liu, Cheng-Yu Hsu, Shan-Hung Wu, Non-linear Cross-domain Collaborative Filtering via Hyper-structure Transfer, Proceedings of the 32nd International Conference on International Conference on Machine Learning, July 06-11, 2015, Lille, France
206. Weike Pan, Li Chen, Group Bayesian Personalized Ranking with Rich Interactions for One-class Collaborative Filtering, Neurocomputing, v.207 N.C, p.501-510, September 2016
207. Soudip Roy Chowdhury, Florian Daniel, Fabio Casati, Recommendation and Weaving of Reusable Mashup Model Patterns for Assisted Development, ACM Transactions on Internet Technology (TOIT), v.14 n.2-3, October 2014
208. Zhe Zhao, Zhiyuan Cheng, Lichan Hong, Ed H. Chi, Improving User Topic Interest Profiles by Behavior Factorization, Proceedings of the 24th International Conference on World Wide Web, May 18-22, 2015, Florence, Italy
209. Konstantin Vorontsov, Oleksandr Frei, Murat Apishev, Peter Romov, Marina Suvorova, Anastasia Yanina, Non-Bayesian Additive Regularization for Multimodal Topic Modeling of Large Collections, Proceedings of the 2015 Workshop on Topic Models: Post-Processing and Applications, October 19-19, 2015, Melbourne, Australia
210. Willie Neiswanger, Chong Wang, Qirong Ho, Eric P. Xing, Modeling Citation Networks Using Latent Random Offsets, Proceedings of the Thirtieth Conference on Uncertainty in Artificial Intelligence, July 23-27, 2014, Quebec City, Quebec, Canada
211. Weinan Zhang, Yifei Rong, Jun Wang, Tianchi Zhu, Xiaofan Wang, Feedback Control of Real-Time Display Advertising, Proceedings of the Ninth ACM International Conference on Web Search and Data Mining, February 22-25, 2016, San Francisco, California, USA
212. Ruining He, Julian McAuley, Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering, Proceedings of the 25th International Conference on World Wide Web, April 11-15, 2016, Montréal, Québec, Canada
213. Maksims Volkovs, Guang Wei Yu, Effective Latent Models for Binary Feedback in Recommender Systems, Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, August 09-13, 2015, Santiago, Chile
214. Hao Wang, Naiyan Wang, Dit-Yan Yeung, Collaborative Deep Learning for Recommender Systems, Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 10-13, 2015, Sydney, NSW, Australia
215. Tong Zhao, H. Vicky Zhao, Irwin King, Exploiting Game Theoretic Analysis for Link Recommendation in Social Networks, Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, October 18-23, 2015, Melbourne, Australia
216. Edson B. Santos Junior, Marcelo G. Manzato, Rudinei Goularte, Hybrid Recommenders: Incorporating Metadata Awareness Into Latent Factor Models, Proceedings of the 19th Brazilian Symposium on Multimedia and the Web, November 05-08, 2013, Salvador, Brazil
217. Bin Liu, Yao Wu, Neil Zhenqiang Gong, Junjie Wu, Hui Xiong, Martin Ester, Structural Analysis of User Choices for Mobile App Recommendation, ACM Transactions on Knowledge Discovery from Data (TKDD), v.11 n.2, p.1-23, December 2016
218. Chen Cheng, Haiqin Yang, Irwin King, Michael R. Lyu, A Unified Point-of-Interest Recommendation Framework in Location-Based Social Networks, ACM Transactions on Intelligent Systems and Technology (TIST), v.8 n.1, p.1-21, October 2016
219. Peifeng Yin, Ping Luo, Wang-Chien Lee, Min Wang, Silence is Also Evidence: Interpreting Dwell Time for Recommendation from Psychological Perspective, Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 11-14, 2013, Chicago, Illinois, USA
220. Yue Shi, Alexandros Karatzoglou, Linas Baltrunas, Martha Larson, Alan Hanjalic, Nuria Oliver, TFMAP: Optimizing MAP for Top-n Context-aware Recommendation, Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, August 12-16, 2012, Portland, Oregon, USA
221. Gang Zhao, Mong Li Lee, Wynne Hsu, Wei Chen, Haoji Hu, Community-based User Recommendation in Uni-directional Social Networks, Proceedings of the 22nd ACM International Conference on Conference on Information & Knowledge Management, October 27-November 01, 2013, San Francisco, California, USA
222. Konstantinos Babas, Georgios Chalkiadakis, Evangelos Tripolitakis, You Are What You Consume: A Bayesian Method for Personalized Recommendations, Proceedings of the 7th ACM Conference on Recommender Systems, October 12-16, 2013, Hong Kong, China
223. Alejandro Bellogín, Iván Cantador, Pablo Castells, A Study of Heterogeneity in Recommendations for a Social Music Service, Proceedings of the 1st International Workshop on Information Heterogeneity and Fusion in Recommender Systems, p.1-8, September 26-26, 2010, Barcelona, Spain
224. Beidou Wang, Martin Ester, Jiajun Bu, Yu Zhu, Ziyu Guan, Deng Cai, Which to View: Personalized Prioritization for Broadcast Emails, Proceedings of the 25th International Conference on World Wide Web, April 11-15, 2016, Montréal, Québec, Canada
225. Yingming Li, Ming Yang, Zhongfei (Mark) Zhang, Scientific Articles Recommendation, Proceedings of the 22nd ACM International Conference on Conference on Information & Knowledge Management, October 27-November 01, 2013, San Francisco, California, USA
226. Xin Wang, Roger Donaldson, Christopher Nell, Peter Gorniak, Martin Ester, Jiajun Bu, Recommending Groups to Users Using User-group Engagement and Time-dependent Matrix Factorization, Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, February 12-17, 2016, Phoenix, Arizona
227. Min Li, Jian Tan, Yandong Wang, Li Zhang, Valentina Salapura, SparkBench: A Comprehensive Benchmarking Suite for in Memory Data Analytic Platform Spark, Proceedings of the 12th ACM International Conference on Computing Frontiers, p.1-8, May 18-21, 2015, Ischia, Italy
228. Shiyu Chang, Yang Zhang, Jiliang Tang, Dawei Yin, Yi Chang, Mark A. Hasegawa-Johnson, Thomas S. Huang, Positive-Unlabeled Learning in Streaming Networks, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 13-17, 2016, San Francisco, California, USA
229. Shiyu Chang, Jiayu Zhou, Pirooz Chubak, Junling Hu, Thomas S. Huang, A Space Alignment Method for Cold-start Tv Show Recommendations, Proceedings of the 24th International Conference on Artificial Intelligence, p.3373-3379, July 25-31, 2015, Buenos Aires, Argentina
230. Dietmar Jannach, Lukas Lerche, Michael Jugovac, Adaptation and Evaluation of Recommendations for Short-term Shopping Goals, Proceedings of the 9th ACM Conference on Recommender Systems, September 16-20, 2015, Vienna, Austria
231. Xiaolan Sha, Daniele Quercia, Pietro Michiardi, Matteo Dell'Amico, Spotting Trends: The Wisdom of the Few, Proceedings of the Sixth ACM Conference on Recommender Systems, September 09-13, 2012, Dublin, Ireland
232. Zhu Zhang, Xiaolong Zheng, Daniel Dajun Zeng, A Framework for Diversifying Recommendation Lists by User Interest Expansion, Knowledge-Based Systems, v.105 N.C, p.83-95, August 2016
233. Qifan Wang, Dan Zhang, Luo Si, Semantic Hashing Using Tags and Topic Modeling, Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, July 28-August 01, 2013, Dublin, Ireland
234. Faraz Makari, Christina Teflioudi, Rainer Gemulla, Peter Haas, Yannis Sismanis, Shared-memory and Shared-nothing Stochastic Gradient Descent Algorithms for Matrix Completion, Knowledge and Information Systems, v.42 n.3, p.493-523, March 2015
235. Xin Liu, Modeling Users' Dynamic Preference for Personalized Recommendation, Proceedings of the 24th International Conference on Artificial Intelligence, p.1785-1791, July 25-31, 2015, Buenos Aires, Argentina
236. Mike Gartrell, Ulrich Paquet, Noam Koenigstein, Bayesian Low-Rank Determinantal Point Processes, Proceedings of the 10th ACM Conference on Recommender Systems, September 15-19, 2016, Boston, Massachusetts, USA
237. Nagarajan Natarajan, Donghyuk Shin, Inderjit S. Dhillon, Which App Will You Use Next?: Collaborative Filtering with Interactional Context, Proceedings of the 7th ACM Conference on Recommender Systems, October 12-16, 2013, Hong Kong, China
238. Katrien Verbert, Denis Parra, Peter Brusilovsky, Erik Duval, Visualizing Recommendations to Support Exploration, Transparency and Controllability, Proceedings of the 2013 International Conference on Intelligent User Interfaces, March 19-22, 2013, Santa Monica, California, USA
239. Tobias Schnabel, Adith Swaminathan, Ashudeep Singh, Navin Chandak, Thorsten Joachims, Recommendations As Treatments: Debiasing Learning and Evaluation, Proceedings of the 33rd International Conference on International Conference on Machine Learning, June 19-24, 2016, New York, NY, USA
240. How Jing, Alexander J. Smola, Neural Survival Recommender, Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, February 06-10, 2017, Cambridge, United Kingdom
241. Michal Aharon, Eshcar Hillel, Amit Kagian, Ronny Lempel, Hayim Makabee, Raz Nissim, Watch-It-Next: A Contextual TV Recommendation System, Proceedings, Part III, of the European Conference on Machine Learning and Knowledge Discovery in Databases, September 07-11, 2015, Porto, Portugal
242. Anastasia Mochalova, Alexandros Nanopoulos, A Targeted Approach to Viral Marketing, Electronic Commerce Research and Applications, v.13 n.4, p.283-294, July, 2014
243. Balázs Hidasi, Domonkos Tikk, Context-aware Item-to-item Recommendation Within the Factorization Framework, Proceedings of the 3rd Workshop on Context-awareness in Retrieval and Recommendation, p.19-25, February 05-05, 2013, Rome, Italy
244. Chen Lin, Runquan Xie, Xinjun Guan, Lei Li, Tao Li, Personalized News Recommendation via Implicit Social Experts, Information Sciences: An International Journal, 254, p.1-18, January, 2014
245. Antonio Hernando, Jesús Bobadilla, Fernando Ortega, A Non Negative Matrix Factorization for Collaborative Filtering Recommender Systems based on a Bayesian Probabilistic Model, Knowledge-Based Systems, v.97 N.C, p.188-202, April 2016
246. Yanxiang Huang, Bin Cui, Jie Jiang, Kunqian Hong, Wenyu Zhang, Yiran Xie, Real-time Video Recommendation Exploration, Proceedings of the 2016 International Conference on Management of Data, June 26-July 01, 2016, San Francisco, California, USA
247. Li Chen, Wei Zeng, Quan Yuan, A Unified Framework for Recommending Items, Groups and Friends in Social Media Environment via Mutual Resource Fusion, Expert Systems with Applications: An International Journal, v.40 n.8, p.2889-2903, June, 2013
248. Tamas Jambor, Jun Wang, Neal Lathia, Using Control Theory for Stable and Efficient Recommender Systems, Proceedings of the 21st International Conference on World Wide Web, April 16-20, 2012, Lyon, France
249. Aäron Van Den Oord, Sander Dieleman, Benjamin Schrauwen, Deep Content-based Music Recommendation, Proceedings of the 26th International Conference on Neural Information Processing Systems, p.2643-2651, December 05-10, 2013, Lake Tahoe, Nevada
250. Nikolaos Pappas, Andrei Popescu-Belis, Combining Content with User Preferences for Non-fiction Multimedia Recommendation: A Study on TED Lectures, Multimedia Tools and Applications, v.74 n.4, p.1175-1197, February 2015
251. Weilong Yao, Jing He, Guangyan Huang, Jie Cao, Yanchun Zhang, A Graph-based Model for Context-aware Recommendation Using Implicit Feedback Data, World Wide Web, v.18 n.5, p.1351-1371, September 2015
252. Brian McFee, Thierry Bertin-Mahieux, Daniel P.W. Ellis, Gert R.G. Lanckriet, The Million Song Dataset Challenge, Proceedings of the 21st International Conference Companion on World Wide Web, April 16-20, 2012, Lyon, France
253. Yue Shi, Pavel Serdyukov, Alan Hanjalic, Martha Larson, Nontrivial Landmark Recommendation Using Geotagged Photos, ACM Transactions on Intelligent Systems and Technology (TIST), v.4 n.3, June 2013
254. Prem Gopalan, Jake M. Hofman, David M. Blei, Scalable Recommendation with Hierarchical Poisson Factorization, Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence, July 12-16, 2015, Amsterdam, Netherlands
255. Defu Lian, Xing Xie, Mining Check-In History for Personalized Location Naming, ACM Transactions on Intelligent Systems and Technology (TIST), v.5 n.2, p.1-25, April 2014
256. Komal Kapoor, Karthik Subbian, Jaideep Srivastava, Paul Schrater, Just in Time Recommendations: Modeling the Dynamics of Boredom in Activity Streams, Proceedings of the Eighth ACM International Conference on Web Search and Data Mining, February 02-06, 2015, Shanghai, China
257. Yue Shi, Alexandros Karatzoglou, Linas Baltrunas, Martha Larson, Alan Hanjalic, CARS2: Learning Context-aware Representations for Context-aware Recommendations, Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, November 03-07, 2014, Shanghai, China
258. Younghoon Kim, Kyuseok Shim, TWILITE: A Recommendation System for Twitter Using a Probabilistic Model based on Latent Dirichlet Allocation, Information Systems, 42, p.59-77, June, 2014
259. Representing Conditional Preference by Boosted Regression Trees for Recommendation, Information Sciences: An International Journal, v.327 N.C, p.1-20, January 2016
260. Xijun Liang, Zhonghang Xia, Liping Pang, Liwei Zhang, Hongwei Zhang, Measure Prediction Capability of Data for Collaborative Filtering, Knowledge and Information Systems, v.49 n.3, p.975-1004, December 2016
261. Wenyu Liu, Caihua Wu, Bin Feng, Juntao Liu, Conditional Preference in Recommender Systems, Expert Systems with Applications: An International Journal, v.42 n.2, p.774-788, February 2015
262. Hongzhi Yin, Bin Cui, Ling Chen, Zhiting Hu, Xiaofang Zhou, Dynamic User Modeling in Social Media Systems, ACM Transactions on Information Systems (TOIS), v.33 n.3, p.1-44, March 2015
263. Martin Saveski, Amin Mantrach, Item Cold-start Recommendations: Learning Local Collective Embeddings, Proceedings of the 8th ACM Conference on Recommender Systems, October 06-10, 2014, Foster City, Silicon Valley, California, USA
264. Ting Yuan, Jian Cheng, Xi Zhang, Qinshan Liu, Hanqing Lu, Enriching One-class Collaborative Filtering with Content Information from Social Media, Multimedia Systems, v.22 n.1, p.51-62, February 2016
265. Alejandro Montes-García, Jose María Álvarez-Rodríguez, Jose Emilio Labra-Gayo, Marcos Martínez-Merino, Towards a Journalist-based News Recommendation System: The Wesomender Approach, Expert Systems with Applications: An International Journal, v.40 n.17, p.6735-6741, December, 2013
266. Paul Seitlinger, Dominik Kowald, Simone Kopeinik, Ilire Hasani-Mavriqi, Elisabeth Lex, Tobias Ley, Attention Please! A Hybrid Resource Recommender Mimicking Attention-Interpretation Dynamics, Proceedings of the 24th International Conference on World Wide Web, May 18-22, 2015, Florence, Italy
267. Shouxian Wei, Xiaolin Zheng, Deren Chen, Chaochao Chen, A Hybrid Approach for Movie Recommendation via Tags and Ratings, Electronic Commerce Research and Applications, v.18 N.C, p.83-94, July 2016
268. Rong Pan, Martin Scholz, Mind the Gaps: Weighting the Unknown in Large-scale One-class Collaborative Filtering, Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, June 28-July 01, 2009, Paris, France
269. Denis Parra, Peter Brusilovsky, User-controllable Personalization, International Journal of Human-Computer Studies, v.78 N.C, p.43-67, June 2015
270. Sebastian Schelter, Stephan Ewen, Kostas Tzoumas, Volker Markl, "All Roads Lead to Rome": Optimistic Recovery for Distributed Iterative Data Processing, Proceedings of the 22nd ACM International Conference on Conference on Information & Knowledge Management, October 27-November 01, 2013, San Francisco, California, USA
271. Vincent W. Zheng, Yu Zheng, Xing Xie, Qiang Yang, Towards Mobile Intelligence: Learning from GPS History Data for Collaborative Recommendation, Artificial Intelligence, 184-185, p.17-37, June, 2012
272. Nicola Barbieri, Giuseppe Manco, Ettore Ritacco, Marco Carnuccio, Antonio Bevacqua, Probabilistic Topic Models for Sequence Data, Machine Learning, v.93 n.1, p.5-29, October 2013
273. Trapit Bansal, Mrinal Das, Chiranjib Bhattacharyya, Content Driven User Profiling for Comment-Worthy Recommendations of News and Blog Articles, Proceedings of the 9th ACM Conference on Recommender Systems, September 16-20, 2015, Vienna, Austria
274. Maryam Khanian Najafabadi, Mohd Naz'ri Mahrin, A Systematic Literature Review on the State of Research and Practice of Collaborative Filtering Technique and Implicit Feedback, Artificial Intelligence Review, v.45 n.2, p.167-201, February 2016
275. Marco Tulio Ribeiro, Anisio Lacerda, Adriano Veloso, Nivio Ziviani, Pareto-efficient Hybridization for Multi-objective Recommender Systems, Proceedings of the Sixth ACM Conference on Recommender Systems, September 09-13, 2012, Dublin, Ireland
276. Youngchul Cha, Bin Bi, Chu-Cheng Hsieh, Junghoo Cho, Incorporating Popularity in Topic Models for Social Network Analysis, Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, July 28-August 01, 2013, Dublin, Ireland
277. Duen-Ren Liu, Hani Omar, Chuen-He Liou, Huai-Chun Chi, Cheng-Ho Hsu, Recommending Blog Articles based on Popular Event Trend Analysis, Information Sciences: An International Journal, v.305 N.C, p.302-319, June 2015
278. Ming Yan, Jitao Sang, Changsheng Xu, M. Shamim Hossain, A Unified Video Recommendation by Cross-Network User Modeling, ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), v.12 n.4, p.1-24, August 2016
279. Xin Li, Hsinchun Chen, Recommendation As Link Prediction in Bipartite Graphs, Decision Support Systems, v.54 n.2, p.880-890, January, 2013
280. Sergio Oramas, Vito Claudio Ostuni, Tommaso Di Noia, Xavier Serra, Eugenio Di Sciascio, Sound and Music Recommendation with Knowledge Graphs, ACM Transactions on Intelligent Systems and Technology (TIST), v.8 n.2, December 2016
281. Majda Maatallah, Hassina Seridi-Bouchelaghem, A Fuzzy Hybrid Approach to Enhance Diversity in Top-N Recommendations, International Journal of Business Information Systems, v.19 n.4, p.505-530, June 2015
282. Cheng-Kang Hsieh, Longqi Yang, Honghao Wei, Mor Naaman, Deborah Estrin, Immersive Recommendation: News and Event Recommendations Using Personal Digital Traces, Proceedings of the 25th International Conference on World Wide Web, April 11-15, 2016, Montréal, Québec, Canada
283. Balázs Hidasi, Domonkos Tikk, Fast ALS-Based Tensor Factorization for Context-aware Recommendation from Implicit Feedback, Proceedings of the 2012 European Conference on Machine Learning and Knowledge Discovery in Databases, September 24-28, 2012, Bristol, UK
284. Alejandro Bellogín, Jun Wang, Pablo Castells, Bridging Memory-based Collaborative Filtering and Text Retrieval, Information Retrieval, v.16 n.6, p.697-724, December 2013
285. Sarik Ghazarian, Mohammad Ali Nematbakhsh, Enhancing Memory-based Collaborative Filtering for Group Recommender Systems, Expert Systems with Applications: An International Journal, v.42 n.7, p.3801-3812, May 2015
286. Xin Wang, Wei Lu, Martin Ester, Can Wang, Chun Chen, Social Recommendation with Strong and Weak Ties, Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, October 24-28, 2016, Indianapolis, Indiana, USA
287. Edita Grolman, Ariel Bar, Bracha Shapira, Lior Rokach, Aviram Dayan, Utilizing Transfer Learning for in-domain Collaborative Filtering, Knowledge-Based Systems, v.107 N.C, p.70-82, September 2016
288. Nicholas Ampazis, Forecasting Demand in Supply Chain Using Machine Learning Algorithms, International Journal of Artificial Life Research, v.5 n.1, p.56-73, January 2015
289. Qian Zhao, Shuo Chang, F. Maxwell Harper, Joseph A. Konstan, Gaze Prediction for Recommender Systems, Proceedings of the 10th ACM Conference on Recommender Systems, September 15-19, 2016, Boston, Massachusetts, USA
290. Trapit Bansal, David Belanger, Andrew McCallum, Ask the GRU: Multi-task Learning for Deep Text Recommendations, Proceedings of the 10th ACM Conference on Recommender Systems, September 15-19, 2016, Boston, Massachusetts, USA
291. Daniel Bernardes, Mamadou Diaby, Raphaël Fournier, Françoise FogelmanSoulié, Emmanuel Viennet, A Social Formalism and Survey for Recommender Systems, ACM SIGKDD Explorations Newsletter, v.16 n.2, December 2014
292. Anisio Lacerda, Rodrygo L. Santos, Adriano Veloso, Nivio Ziviani, Improving Daily Deals Recommendation Using Explore-then-exploit Strategies, Information Retrieval, v.18 n.2, p.95-122, April 2015
293. Gawesh Jawaheer, Peter Weller, Patty Kostkova, Modeling User Preferences in Recommender Systems: A Classification Framework for Explicit and Implicit User Feedback, ACM Transactions on Interactive Intelligent Systems (TiiS), v.4 n.2, p.1-26, July 2014
294. Rafael Pereira, Hélio Lopes, Karin Breitman, Vicente Mundim, Wandenberg Peixoto, Cloud based Real-time Collaborative Filtering for Item-item Recommendations, Computers in Industry, v.65 n.2, p.279-290, February, 2014
295. Fernando Ortega, Jesús Bobadilla, Antonio Hernando, Fernando Rodríguez, Using Hierarchical Graph Maps to Explain Collaborative Filtering Recommendations, International Journal of Intelligent Systems, v.29 n.5, p.462-477, May 2014
296. Jianjun Yu, Tongyu Zhu, Combining Long-term and Short-term User Interest for Personalized Hashtag Recommendation, Frontiers of Computer Science: Selected Publications from Chinese Universities, v.9 n.4, p.608-622, August 2015
297. Xavier Amatriain, Mining Large Streams of User Data for Personalized Recommendations, ACM SIGKDD Explorations Newsletter, v.14 n.2, December 2012
298. Marco Tulio Ribeiro, Nivio Ziviani, Edleno Silva De Moura, Itamar Hata, Anisio Lacerda, Adriano Veloso, Multiobjective Pareto-Efficient Approaches for Recommender Systems, ACM Transactions on Intelligent Systems and Technology (TIST), v.5 n.4, January 2015
299. Matteo Dell'Amico, Licia Capra, Dependable Filtering: Philosophy and Realizations, ACM Transactions on Information Systems (TOIS), v.29 n.1, p.1-37, December 2010
300. Nachiketa Sahoo, Param Vir Singh, Tridas Mukhopadhyay, A Hidden Markov Model for Collaborative Filtering, MIS Quarterly, v.36 n.4, p.1329-1356, December 2012
301. Alejandro BellogíN, IváN Cantador, Pablo Castells, A Comparative Study of Heterogeneous Item Recommendations in Social Systems, Information Sciences: An International Journal, 221, p.142-169, February, 2013
302. Katrien Verbert, Denis Parra, Peter Brusilovsky, Agents Vs. Users: Visual Recommendation of Research Talks with Multiple Dimension of Relevance, ACM Transactions on Interactive Intelligent Systems (TiiS), v.6 n.2, August 2016
303. Ignacio Fernández-Tobías, Matthias Braunhofer, Mehdi Elahi, Francesco Ricci, Iván Cantador, Alleviating the New User Problem in Collaborative Filtering by Exploiting Personality Information, User Modeling and User-Adapted Interaction, v.26 n.2-3, p.221-255, June 2016
304. Sheng Li, Jaya Kawale, Yun Fu, Deep Collaborative Filtering via Marginalized Denoising Auto-encoder, Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, October 18-23, 2015, Melbourne, Australia
305. Hui Li, Jiangtao Cui, Bingqing Shen, Jianfeng Ma, An Intelligent Movie Recommendation System through Group-level Sentiment Analysis in Microblogs, Neurocomputing, v.210 N.C, p.164-173, October 2016
306. Mahdi Nasiri, Behrouz Minaei, Increasing Prediction Accuracy in Collaborative Filtering with Initialized Factor Matrices, The Journal of Supercomputing, v.72 n.6, p.2157-2169, June 2016
307. Xinxi Wang, Ye Wang, Improving Content-based and Hybrid Music Recommendation Using Deep Learning, Proceedings of the ACM International Conference on Multimedia, November 03-07, 2014, Orlando, Florida, USA
308. Bart P. Knijnenburg, Martijn C. Willemsen, Zeno Gantner, Hakan Soncu, Chris Newell, Explaining the User Experience of Recommender Systems, User Modeling and User-Adapted Interaction, v.22 n.4-5, p.441-504, October 2012
309. Jianshan Sun, Gang Wang, Xusen Cheng, Yelin Fu, Mining Affective Text to Improve Social Media Item Recommendation, Information Processing and Management: An International Journal, v.51 n.4, p.444-457, July 2015
310. Makoto Nakatsuji, Hiroyuki Toda, Hiroshi Sawada, Jin Guang Zheng, James A. Hendler, Semantic Sensitive Tensor Factorization, Artificial Intelligence, v.230 N.C, p.224-245, January 2016
311. João Vinagre, Alípio Mário Jorge, João Gama, An Overview on the Exploitation of Time in Collaborative Filtering, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, v.5 n.5, p.195-215, September 2015
312. J. Bobadilla, F. Ortega, A. Hernando, A. GutiéRrez, Recommender Systems Survey, Knowledge-Based Systems, 46, p.109-132, July, 2013
313.

<a Href="signin.cfm?CFID=733195048&CFTOKEN=68893894">

}};

	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2008 CollaborativeFilteringforImplic	Yehuda Koren Chris Volinsky Yifan Hu			Collaborative Filtering for Implicit Feedback Datasets				10.1109/ICDM.2008.22		2008