About cookies on this site Our websites require some cookies to function properly (required). In addition, other cookies may be used with your consent to analyze site usage, improve the user experience and for advertising. For more information, please review your options. By visiting our website, you agree to our processing of information as described in IBM’sprivacy statement. To provide a smooth navigation, your cookie preferences will be shared across the IBM web domains listed here.
Publication
WWW 2011
Conference paper
Smart news feeds for social networks using scalable joint latent factor models
Abstract
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. © 2011 Authors.