2 research outputs found

    Learning from User Interactions for Recommending Content in Social Media

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    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.
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