Latent Collaborative Retrieval
This addresses a common but underexplored task in retrieval and recommendation systems, though it appears incremental as it adapts existing factorization methods to a new tensor setup.
The paper tackles the joint problem of recommending items to a user based on a query, introducing a factorized model that optimizes top-ranked items and outperforms baselines in empirical results.
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.