Learning from user interactions for recommending content in social media

Abstract

We study the problem of recommending hyperlinks to users in social media in the form of status updates. We start with a candidate set of links posted by a user's social circle (e.g., friends, followers) and rank these links using a combination of (i) a user interaction model, and (ii) the similarity of a user profile and a candidate link. Experiments on two datasets demonstrate that our method is robust and, on average, outperforms, a strong chronological baseline. © 2014 Springer International Publishing Switzerland.