LA-CTR: A Limited Attention Collaborative Topic Regression for Social Media

TL;DR

This paper introduces LA-CTR, a probabilistic model that incorporates limited human attention into social media recommendation systems, improving prediction accuracy over existing models.

Contribution

The paper presents a novel collaborative topic regression model that accounts for non-uniform limited attention, enhancing user preference learning in social media.

Findings

LA-CTR outperforms state-of-the-art models in predicting user votes.

Incorporating attention improves the understanding of online social behavior.

Psychologically motivated models better predict observed social media interactions.

Abstract

Probabilistic models can learn users' preferences from the history of their item adoptions on a social media site, and in turn, recommend new items to users based on learned preferences. However, current models ignore psychological factors that play an important role in shaping online social behavior. One such factor is attention, the mechanism that integrates perceptual and cognitive features to select the items the user will consciously process and may eventually adopt. Recent research has shown that people have finite attention, which constrains their online interactions, and that they divide their limited attention non-uniformly over other people. We propose a collaborative topic regression model that incorporates limited, non-uniformly divided attention. We show that the proposed model is able to learn more accurate user preferences than state-of-art models, which do not take human cognitive factors into account. Specifically we analyze voting on news items on the social news aggregator and show that our model is better able to predict held out votes than alternate models. Our study demonstrates that psycho-socially motivated models are better able to describe and predict observed behavior than models which only consider latent social structure and content.