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• W3141535659 abstract "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 purchasing behavior better, several problematic model designs and assumptions of the existing interaction-based models lead to its suboptimal performance compared to existing siamese models. In this paper, we identify three problems of the existing interaction-based recommendation models and propose a couple of solutions as well as a new interaction-based model to incorporate review data for rating prediction. Our model implements a relevance matching model with regularized training losses to discover user relevant information from long item reviews, and it also adapts a zero attention strategy to dynamically balance the item-dependent and item-independent information extracted from user reviews. Empirical experiments and case studies on Amazon Product Benchmark datasets show that our model can extract effective and interpretable user/item representations from their reviews and outperforms multiple types of state-of-the-art review-based recommendation models." @default.
• W3141535659 created "2021-04-13" @default.
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• W3141535659 date "2021-01-01" @default.
• W3141535659 modified "2023-10-02" @default.
• W3141535659 title "A Zero Attentive Relevance Matching Network for Review Modeling in Recommendation System" @default.
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• W3141535659 doi "https://doi.org/10.1007/978-3-030-72113-8_48" @default.
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