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        <title>Neural News Recommendation with Topic-Aware News Representation</title>
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    <name type="personal">
        <namePart type="given">Chuhan</namePart>
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    <name type="personal">
        <namePart type="given">Fangzhao</namePart>
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    <name type="personal">
        <namePart type="given">Mingxiao</namePart>
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    <name type="personal">
        <namePart type="given">Yongfeng</namePart>
        <namePart type="family">Huang</namePart>
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    <name type="personal">
        <namePart type="given">Xing</namePart>
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        <dateIssued>2019-07</dateIssued>
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    <abstract>News recommendation can help users find interested news and alleviate information overload. The topic information of news is critical for learning accurate news and user representations for news recommendation. However, it is not considered in many existing news recommendation methods. In this paper, we propose a neural news recommendation approach with topic-aware news representations. The core of our approach is a topic-aware news encoder and a user encoder. In the news encoder we learn representations of news from their titles via CNN networks and apply attention networks to select important words. In addition, we propose to learn topic-aware news representations by jointly training the news encoder with an auxiliary topic classification task. In the user encoder we learn the representations of users from their browsed news and use attention networks to select informative news for user representation learning. Extensive experiments on a real-world dataset validate the effectiveness of our approach.</abstract>
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    <identifier type="doi">10.18653/v1/P19-1110</identifier>
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        <date>2019-07</date>
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%0 Conference Proceedings
%T Neural News Recommendation with Topic-Aware News Representation
%A Wu, Chuhan
%A Wu, Fangzhao
%A An, Mingxiao
%A Huang, Yongfeng
%A Xie, Xing
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F wu-etal-2019-neural-news-recommendation
%X News recommendation can help users find interested news and alleviate information overload. The topic information of news is critical for learning accurate news and user representations for news recommendation. However, it is not considered in many existing news recommendation methods. In this paper, we propose a neural news recommendation approach with topic-aware news representations. The core of our approach is a topic-aware news encoder and a user encoder. In the news encoder we learn representations of news from their titles via CNN networks and apply attention networks to select important words. In addition, we propose to learn topic-aware news representations by jointly training the news encoder with an auxiliary topic classification task. In the user encoder we learn the representations of users from their browsed news and use attention networks to select informative news for user representation learning. Extensive experiments on a real-world dataset validate the effectiveness of our approach.
%R 10.18653/v1/P19-1110
%U https://aclanthology.org/P19-1110
%U https://doi.org/10.18653/v1/P19-1110
%P 1154-1159

Markdown (Informal)

[Neural News Recommendation with Topic-Aware News Representation](https://aclanthology.org/P19-1110) (Wu et al., ACL 2019)

• Neural News Recommendation with Topic-Aware News Representation (Wu et al., ACL 2019)

ACL

• Chuhan Wu, Fangzhao Wu, Mingxiao An, Yongfeng Huang, and Xing Xie. 2019. Neural News Recommendation with Topic-Aware News Representation. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 1154–1159, Florence, Italy. Association for Computational Linguistics.