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        <title>Hierarchical User and Item Representation with Three-Tier Attention for Recommendation</title>
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        <namePart type="given">Chuhan</namePart>
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    <name type="personal">
        <namePart type="given">Fangzhao</namePart>
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    <name type="personal">
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    <name type="personal">
        <namePart type="given">Yongfeng</namePart>
        <namePart type="family">Huang</namePart>
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    <abstract>Utilizing reviews to learn user and item representations is useful for recommender systems. Existing methods usually merge all reviews from the same user or for the same item into a long document. However, different reviews, sentences and even words usually have different informativeness for modeling users and items. In this paper, we propose a hierarchical user and item representation model with three-tier attention to learn user and item representations from reviews for recommendation. Our model contains three major components, i.e., a sentence encoder to learn sentence representations from words, a review encoder to learn review representations from sentences, and a user/item encoder to learn user/item representations from reviews. In addition, we incorporate a three-tier attention network in our model to select important words, sentences and reviews. Besides, we combine the user and item representations learned from the reviews with user and item embeddings based on IDs as the final representations to capture the latent factors of individual users and items. Extensive experiments on four benchmark datasets validate the effectiveness of our approach.</abstract>
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    <identifier type="doi">10.18653/v1/N19-1180</identifier>
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%0 Conference Proceedings
%T Hierarchical User and Item Representation with Three-Tier Attention for Recommendation
%A Wu, Chuhan
%A Wu, Fangzhao
%A Liu, Junxin
%A Huang, Yongfeng
%Y Burstein, Jill
%Y Doran, Christy
%Y Solorio, Thamar
%S Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F wu-etal-2019-hierarchical
%X Utilizing reviews to learn user and item representations is useful for recommender systems. Existing methods usually merge all reviews from the same user or for the same item into a long document. However, different reviews, sentences and even words usually have different informativeness for modeling users and items. In this paper, we propose a hierarchical user and item representation model with three-tier attention to learn user and item representations from reviews for recommendation. Our model contains three major components, i.e., a sentence encoder to learn sentence representations from words, a review encoder to learn review representations from sentences, and a user/item encoder to learn user/item representations from reviews. In addition, we incorporate a three-tier attention network in our model to select important words, sentences and reviews. Besides, we combine the user and item representations learned from the reviews with user and item embeddings based on IDs as the final representations to capture the latent factors of individual users and items. Extensive experiments on four benchmark datasets validate the effectiveness of our approach.
%R 10.18653/v1/N19-1180
%U https://aclanthology.org/N19-1180
%U https://doi.org/10.18653/v1/N19-1180
%P 1818-1826

Markdown (Informal)

[Hierarchical User and Item Representation with Three-Tier Attention for Recommendation](https://aclanthology.org/N19-1180) (Wu et al., NAACL 2019)

• Hierarchical User and Item Representation with Three-Tier Attention for Recommendation (Wu et al., NAACL 2019)

ACL

• Chuhan Wu, Fangzhao Wu, Junxin Liu, and Yongfeng Huang. 2019. Hierarchical User and Item Representation with Three-Tier Attention for Recommendation. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 1818–1826, Minneapolis, Minnesota. Association for Computational Linguistics.