Neural Item Recommendation Algorithm

A Neural Item Recommendation Algorithm is an item recommendation algorithm that ... A Neural Recommendation Algorithm is an items recommendation algorithm that is based on a deep neural network algorithm.

• Context:
  • It can be implemented by a Deep Neural Network-based Recommendation System.
• Example(s):
  • a Collaborative Deep Learning Algorithm.
  • (He et al., 2017).
  • (We et al., 2017).
  • (Guo et al., 2017).
  • (Wu et al., 2016).
  • (Zheng et al., 2016).
  • (Cheng et al., 2016).
  • (Wang et al., 2015).
  • …
• Counter-Example(s):
  • Matrix Factorization-based Recommendation Algorithm.
• See: DropoutNet Algorithm, Domain Specific Recommendation Task, Algorithm-Specific Task.

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References

2018a

• (Rybakov et al., 2018) ⇒ Oleg Rybakov, Vijai Mohan, Avishkar Misra, Scott LeGrand, Rejith Joseph, Kiuk Chung, Siddharth Singh, Qian You, Eric Nalisnick, Leo Dirac, and Runfei Luo. (2018). “The Effectiveness of a Two-layer Neural Network for Recommendations.”

2017a

• (Fawaz et al., 2017) ⇒ Nadia Fawaz, Saurabh Kataria, Benjamin Le, Liang Zhang, and Ganesh Venkataraman. (2017). “Deep Learning for Personalized Search and Recommender Systems." Slides from KDD-2017 Tutorial
  • QUOTE: In the area of personalized recommender systems, deep learning has started showing promising advances in recent years. The key to success of deep learning in personalized recommender systems is its ability to learn distributed representations of users’ and items’ attributes in low dimensional dense vector space and combine these to recommend relevant items to users.

2017b

• (He et al., 2017) ⇒ Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. (2017). “Neural Collaborative Filtering.” In: Proceedings of the 26th International Conference on World Wide Web (WWW-2017).

2017c

• (We et al., 2017) ⇒ Chao-Yuan Wu, Amr Ahmed, Alex Beutel, Alexander J Smola, and How Jing. (2017). “Recurrent Recommender Networks.” In: Proceedings of WSDM 2017.

2017d

• (Zheng et al., 2017) ⇒ Lei Zheng, Vahid Noroozi, and Philip S. Yu. (2017). “Joint Deep Modeling of Users and Items Using Reviews for Recommendation.” In: Proceedings of the Tenth ACM International Conference on Web Search and Data Mining. ISBN:978-1-4503-4675-7 doi:10.1145/3018661.3018665

2017d

• (Guo et al., 2017) ⇒ Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, and Xiuqiang He. (2017). “DeepFM: A Factorization-machine based Neural Network for CTR Prediction.” In: Proceedings of the 26th International Joint Conference on Artificial Intelligence. ISBN:978-0-9992411-0-3

2016a

• (Wu et al., 2016) ⇒ Yao Wu, Christopher DuBois, Alice X. Zheng, and Martin Ester. (2016). “Collaborative Denoising Auto-Encoders for Top-N Recommender Systems.” In: Proceedings of the Ninth ACM International Conference on Web Search and Data Mining. ISBN:978-1-4503-3716-8 doi:10.1145/2835776.2835837
  • QUOTE: Most real-world recommender services measure their performance based on the top-N results shown to the end users. Thus, advances in top-N recommendation have far-ranging consequences in practical applications. In this paper, we present a novel method, called Collaborative Denoising Auto-Encoder (CDAE), for top-N recommendation that utilizes the idea of Denoising Auto-Encoders.

2016b

• (Wang et al., 2016) ⇒ Hao Wang, S. H. I . Xingjian, and Dit-Yan Yeung. (2016). “Collaborative Recurrent Autoencoder: Recommend While Learning to Fill in the Blanks.” In: Advances in Neural Information Processing Systems, (NIPS 2016)

2016c

• (Zheng et al., 2016) ⇒ Yin Zheng, Bangsheng Tang, Wenkui Ding, and Hanning Zhou. (2016). “A Neural Autoregressive Approach to Collaborative Filtering.” Proceedings of ICML 2016 (ICML-2016).

2016d

• (Cheng et al., 2016) ⇒ Heng-Tze Cheng, Levent Koc, Jeremiah Harmsen, Tal Shaked, Tushar Chandra, Hrishi Aradhye, Glen Anderson, Greg Corrado, Wei Chai, Mustafa Ispir, Rohan Anil, Zakaria Haque, Lichan Hong, Vihan Jain, Xiaobing Liu, and Hemal Shah. (2016). “Wide & Deep Learning for Recommender Systems.” In: Proceedings of the 1st Workshop on Deep Learning for Recommender Systems. ISBN:978-1-4503-4795-2 doi:10.1145/2988450.2988454

2015

• (Wang et al., 2015) ⇒ Hao Wang, Naiyan Wang, and Dit-Yan Yeung. (2015). “Collaborative Deep Learning for Recommender Systems.” In: Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2015). ISBN:978-1-4503-3664-2 doi:10.1145/2783258.2783273
  • QUOTE: Collaborative filtering (CF) is a successful approach commonly used by many recommender systems. Conventional CF-based methods use the ratings given to items by users as the sole source of information for learning to make recommendation. However, the ratings are often very sparse in many applications, causing CF-based methods to degrade significantly in their recommendation performance. To address this sparsity problem, auxiliary information such as item content information may be utilized. Collaborative topic regression (CTR) is an appealing recent method taking this approach which tightly couples the two components that learn from two different sources of information. Nevertheless, the latent representation learned by CTR may not be very effective when the auxiliary information is very sparse. To address this problem, we generalize recently advances in deep learning from i.i.d. input to non-i.i.d. (CF-based) input and propose in this paper a hierarchical Bayesian model called collaborative deep learning (CDL), which jointly performs deep representation learning for the content information and collaborative filtering for the ratings (feedback) matrix.

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