  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.
