Learning from user interactions for recommending content in social media

We study the problem of recommending hyperlinks to users in social media. 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