Latent Collaborative Retrieval
Venue
International Conference on Machine Learning (2012)
Publication Year
2012
Authors
Jason Weston, Chong Wang, Ron Weiss, Adam Berenzweig
BibTeX
Abstract
Retrieval tasks typically require a ranking of items given a query. Collaborative
filtering tasks, on the other hand, learn models comparing users with 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 × user × item
tensor for training instead of the more traditional user × 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.
