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Asymmetrical Hierarchical Networks with Attentive Interactions for Interpretable Review-Based Recommendation
[article]
2019
arXiv
pre-print
Recently, recommender systems have been able to emit substantially improved recommendations by leveraging user-provided reviews. Existing methods typically merge all reviews of a given user or item into a long document, and then process user and item documents in the same manner. In practice, however, these two sets of reviews are notably different: users' reviews reflect a variety of items that they have bought and are hence very heterogeneous in their topics, while an item's reviews pertain
arXiv:2001.04346v1
fatcat:dj2a6g4uqzfjpgd7uanx56l3v4