Content based social behavior prediction: a multi-task learning approach

Authors:
Hongliang Fei;Ruoyi Jiang;Yuhao Yang;Bo Luo;Jun Huan
Affiliations:
University of Kansas, Lawrence, KS, USA;University of Kansas, Lawrence, KS, USA;University of Kansas, Lawrence, KS, USA;University of Kansas, Lawrence, KS, USA;University of Kansas, Lawrence, KS, USA
Venue:
Proceedings of the 20th ACM international conference on Information and knowledge management
Year:
2011

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Abstract

Information Flow Studies analyze the principles and mechanisms of social information distribution and is an essential research topic in social networks. Traditional approaches are primarily based on the social network graph topology. However, topology itself can not accurately reflect the user interests or activities. In this paper, we adopt a "microeconomics" approach to study social information diffusion and aim to answer the question that how social information flow and socialization behaviors are related to content similarity and user interests. In particular, we study content-based social activity prediction, i.e., to predict a user's response (e.g. comment or like) to their friends' postings (e.g. blogs) w.r.t. message content. In our solution, we cast the social behavior prediction problem as a multi-task learning problem, in which each task corresponds to a user. We have designed a novel multi-task learning algorithm that is specifically designed for learning information flow in social networks. In our model, we apply l1 and Tikhonov regularization to obtain a sparse and smooth model in a linear multi-task learning framework. Using comprehensive experimental study, we have demonstrated the effectiveness of the proposed learning method.