| Author | Title | Year | Journal/Proceedings | Reftype | DOI/URL |
|---|---|---|---|---|---|
| Weston, J., Wang, C., Weiss, R. & Berenzweig, A. | Latent Collaborative Retrieval | 2012 | misc | URL | |
| 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. | |||||
BibTeX:
@misc{weston2012latent,
author = {Weston, Jason and Wang, Chong and Weiss, Ron and Berenzweig, Adam},
title = {Latent Collaborative Retrieval},
year = {2012},
note = {cite arxiv:1206.4603Comment: ICML2012},
url = {http://arxiv.org/abs/1206.4603}
}
|
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