A Zero Attentive Relevance Matching Networkfor Review Modeling in Recommendation System

@article{Zeng2021AZA,
  title={A Zero Attentive Relevance Matching Networkfor Review Modeling in Recommendation System},
  author={Hansi Zeng and Zhichao Xu and Qingyao Ai},
  journal={ArXiv},
  year={2021},
  volume={abs/2101.06387}
}
User and item reviews are valuable for the construction of recommender systems. In general, existing review-based methods for recommendation can be broadly categorized into two groups: the siamese models that build static user and item representations from their reviews respectively, and the interaction-based models that encode user and item dynamically according to the similarity or relationships of their reviews. Although the interaction-based models have more model capacity and fit human… Expand

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