Abstract

We propose a method to predict users’ interests in social media, using time-evolving, multinomial re-lational data. We exploit various actions performed by users, and their preferences to predict user in-terests. Actions performed by users in social me-dia such as Twitter, Delicious and Facebook have two fundamental properties. (a) User actions can be represented as high-dimensional or multinomial relations - e.g. referring URLs, bookmarking and tagging, clicking a favorite button on a post etc.(b) User actions are time-varying and user-specific– each user has unique preferences that change over time. Consequently, it is appropriate to rep-resent each user’s action at some point in time as a multinomial relational data. We propose Ac-tionGraph, a novel graph representation for model-ing users’ multinomial, time-varying actions. Each user’s action at some time point is represented by an action node. ActionGraph is a bipartite graph whose edges connect an action node to its involv-ing entities, referred to as object nodes. Using real-world social media data, we empirically justify the proposed graph structure. Our experimental re-sults show that the proposed ActionGraph improves the accuracy in a user interest prediction task by outperforming several baselines including standard tensor analysis, a previously proposed state-of-the-art LDA-based method and other graph-based vari-ants. Moreover, the proposed method shows robust performances in the presence of sparse data.