LA-LDA: A Limited Attention Topic Model for Social Recommendation

TL;DR

LA-LDA is a novel topic model that accounts for users' limited and non-uniform attention in social networks, improving the prediction of information diffusion and user behavior.

Contribution

It introduces a limited attention mechanism into topic modeling for social recommendation, enhancing the accuracy of user and diffusion behavior predictions.

Findings

LA-LDA outperforms traditional models in predicting votes on Digg.

Incorporating limited attention improves the modeling of social influence.

Psycho-social factors enhance the understanding of information spread.

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 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.