@misc{weston2012latent,
  abstract = {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.},
  author = {Weston, Jason and Wang, Chong and Weiss, Ron and Berenzweig, Adam},
  interhash = {d0ea194dd0e3a6f35c578439efcb8bff},
  intrahash = {79c6771a9b032497635d5f39a39e921a},
  note = {cite arxiv:1206.4603Comment: ICML2012},
  title = {Latent Collaborative Retrieval},
  url = {http://arxiv.org/abs/1206.4603},
  year = 2012
}