%0 Generic
%1 weston2012latent
%A Weston, Jason
%A Wang, Chong
%A Weiss, Ron
%A Berenzweig, Adam
%D 2012
%K recommender tensor toread
%T Latent Collaborative Retrieval
%U http://arxiv.org/abs/1206.4603
%X Retrieval tasks typically require a ranking of items given a query. Collaborative filtering tasks, on the other hand, learn to model user's preferences over items. In this paper we study the joint problem of recommending items to a user with respect to a given query, which is a surprisingly common task. This setup differs from the standard collaborative filtering one in that we are given a query x user x item tensor for training instead of the more traditional user x item matrix. Compared to document retrieval we do have a query, but we may or may not have content features (we will consider both cases) and we can also take account of the user's profile. We introduce a factorized model for this new task that optimizes the top-ranked items returned for the given query and user. We report empirical results where it outperforms several baselines.