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arXiv · 1206.4603

Latent Collaborative Retrieval

Jason Weston·Chong Wang·Ron Weiss·Adam Berenzweig
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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.

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Jason Weston, Chong Wang, Ron Weiss, Adam Berenzweig. 2012-06-18. Latent Collaborative Retrieval. https://arxiv.org/abs/1206.4603

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