2012 ComSocAdaptiveTransferofUserBeh
- (Zhong et al., 2012) ⇒ Erheng Zhong, Wei Fan, Junwei Wang, Lei Xiao, and Yong Li. (2012). “ComSoc: Adaptive Transfer of User Behaviors over Composite Social Network.” In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2012). ISBN:978-1-4503-1462-6 doi:10.1145/2339530.2339641
Subject Headings:
Notes
Cited By
- http://scholar.google.com/scholar?q=%222012%22+ComSoc%3A+Adaptive+Transfer+of+User+Behaviors+over+Composite+Social+Network
- http://dl.acm.org/citation.cfm?id=2339530.2339641&preflayout=flat#citedby
Quotes
Author Keywords
Abstract
Accurate prediction of user behaviors is important for many social media applications, including social marketing, personalization and recommendation, etc. A major challenge lies in that, the available behavior data or interactions between users and items in a given social network are usually very limited and sparse (e.g., >= 99.9% empty). Many previous works model user behavior from only historical user logs. We observe that many people are members of several social networks in the same time, such as Facebook, Twitter and Tencent's QQ. Importantly, their behaviors and interests in different networks influence one another. This gives us an opportunity to leverage the knowledge of user behaviors in different networks, in order to alleviate the data sparsity problem, and enhance the predictive performance of user modeling. Combining different networks “simply and naively” does not work well. Instead, we formulate the problem to model multiple networks as “composite network knowledge transfer ". We first select the most suitable networks inside a composite social network via a hierarchical Bayesian model, parameterized for individual users, and then build topic models for user behavior prediction using both the relationships in the selected networks and related behavior data. To handle big data, we have implemented the algorithm using Map / Reduce. We demonstrate that the proposed composite network-based user behavior model significantly improve the predictive accuracy over a number of existing approaches on several real world applications, such as a very large social-networking dataset from Tencent Inc.
References
;
Author | volume | Date Value | title | type | journal | titleUrl | doi | note | year | |
---|---|---|---|---|---|---|---|---|---|---|
2012 ComSocAdaptiveTransferofUserBeh | Wei Fan Erheng Zhong Junwei Wang Lei Xiao Yong Li | ComSoc: Adaptive Transfer of User Behaviors over Composite Social Network | 10.1145/2339530.2339641 | 2012 |