LA-LDA: a limited attention topic model for social recommendation

Authors:
Jeon-Hyung Kang;Kristina Lerman;Lise Getoor
Affiliations:
USC Information Sciences Institute, Marina del Rey, CA;USC Information Sciences Institute, Marina del Rey, CA;Computer Science Department, University of Maryland, College Park, MD
Venue:
SBP'13 Proceedings of the 6th international conference on Social Computing, Behavioral-Cultural Modeling and Prediction
Year:
2013

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Abstract

Social media users have finite attention which limits the number of incoming messages from friends they can process. Moreover, they pay more attention to opinions and recommendations of some friends more than others. In this paper, we propose $\mathcal LA$-LDA, a latent topic model which incorporates limited, non-uniformly divided attention in the diffusion process by which opinions and information spread on the social network. We show that our proposed model is able to learn more accurate user models from users' social network and item adoption behavior than models which do not take limited attention into account. We analyze voting on news items on the social news aggregator Digg and show that our proposed model is better able to predict held out votes than alternative models. Our study demonstrates that psycho-socially motivated models have better ability to describe and predict observed behavior than models which only consider topics.