The Journal of Machine Learning Research
Google news personalization: scalable online collaborative filtering
Proceedings of the 16th international conference on World Wide Web
Predictive discrete latent factor models for large scale dyadic data
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
On the quality of inferring interests from social neighbors
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Predicting responses to microblog posts
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
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Social networks such as Facebook and Twitter offer a huge opportunity to tap the collective wisdom (both published and yet to be published) of all the participating users in order to address the information needs of individual users in a highly contextualized fashion using rich user-specific information. Realizing this opportunity, however, requires addressing two key limitations of current social networks: (a) difficulty in discovering relevant content beyond the immediate neighborhood, (b) lack of support for information filtering based on semantics, content source and linkage. We propose a scalable framework for constructing smart news feeds based on predicting user-post relevance using multiple signals such as text content and attributes of users and posts, and various user-user, post-post and user-post relations (e.g. friend, comment, author relations). Our solution comprises of two steps where the first step ensures scalability by selecting a small set of user-post dyads with potentially interesting interactions using inverted feature indexes. The second step models the interactions associated with the selected dyads via a joint latent factor model, which assumes that the user/post content and relationships can be effectively captured by a common latent representation of the users and posts. Experiments on a Facebook dataset using the proposed model lead to improved precision/recall on relevant posts indicating potential for constructing superior quality news feeds.