  While a user's preference is directly reflected in the interactive choice
process between her and the recommender, this wealth of information was not
fully exploited for learning recommender models. In particular, existing
collaborative filtering (CF) approaches take into account only the binary
events of user actions but totally disregard the contexts in which users'
decisions are made. In this paper, we propose Collaborative Competitive
Filtering (CCF), a framework for learning user preferences by modeling the
choice process in recommender systems. CCF employs a multiplicative latent
factor model to characterize the dyadic utility function. But unlike CF, CCF
models the user behavior of choices by encoding a local competition effect. In
this way, CCF allows us to leverage dyadic data that was previously lumped
together with missing data in existing CF models. We present two formulations
and an efficient large scale optimization algorithm. Experiments on three
real-world recommendation data sets demonstrate that CCF significantly
outperforms standard CF approaches in both offline and online evaluations.
