  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.
