TY  - GEN
AU  - Weston, Jason
AU  - Wang, Chong
AU  - Weiss, Ron
AU  - Berenzweig, Adam
A2  - 
T1  - Latent Collaborative Retrieval
JO  - 
PB  - 
AD  - 
PY  - 2012/
VL  - 
IS  - 
SP  - 
EP  - 
UR  - http://arxiv.org/abs/1206.4603
M3  - 
KW  - recommender
KW  - tensor
KW  - toread
L1  - 
N1  - Latent Collaborative Retrieval
N1  - 
AB  - 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.
ER  -