The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
A recomendação personalizada de notícias está se tornando cada vez mais importante para as plataformas de notícias online, para ajudar os usuários a aliviar a sobrecarga de informações e melhorar a experiência de leitura de notícias. Um problema fundamental na recomendação de notícias é aprender representações precisas dos usuários para capturar seu interesse. No entanto, a maioria dos métodos de recomendação de notícias existentes geralmente aprendem a representação do usuário apenas a partir do histórico de notícias interagidas, ignorando os recursos de agrupamento entre os usuários. Aqui propusemos uma rede hash hierárquica de preferência do usuário para melhorar a representação do interesse dos usuários. Na parte hash, uma série de buckets são gerados com base no histórico de interações dos usuários. Usuários com preferências semelhantes são atribuídos automaticamente aos mesmos buckets. Também aprendemos representações de usuários a partir das notícias navegadas na parte histórica. E então, uma Atenção de Rota é adotada para combinar essas duas partes (vetor histórico e vetor hash) e obter o vetor de preferência do usuário mais informativo. Quanto à representação de notícias, um transformador modificado com incorporação de categorias é explorado para construir a representação semântica de notícias. Ao comparar a rede hash hierárquica com vários métodos de recomendação de notícias e conduzir vários experimentos no Microsoft News Dataset (MIND), validamos a eficácia de nossa abordagem na recomendação de notícias.
Jianyong DUAN
North China University of Technology,CNONIX National Standard Application and Promotion Lab
Liangcai LI
North China University of Technology,CNONIX National Standard Application and Promotion Lab
Mei ZHANG
North China University of Technology
Hao WANG
North China University of Technology,CNONIX National Standard Application and Promotion Lab
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Jianyong DUAN, Liangcai LI, Mei ZHANG, Hao WANG, "Hierarchical Preference Hash Network for News Recommendation" in IEICE TRANSACTIONS on Information,
vol. E105-D, no. 2, pp. 355-363, February 2022, doi: 10.1587/transinf.2021EDP7034.
Abstract: Personalized news recommendation is becoming increasingly important for online news platforms to help users alleviate information overload and improve news reading experience. A key problem in news recommendation is learning accurate user representations to capture their interest. However, most existing news recommendation methods usually learn user representation only from their interacted historical news, while ignoring the clustering features among users. Here we proposed a hierarchical user preference hash network to enhance the representation of users' interest. In the hash part, a series of buckets are generated based on users' historical interactions. Users with similar preferences are assigned into the same buckets automatically. We also learn representations of users from their browsed news in history part. And then, a Route Attention is adopted to combine these two parts (history vector and hash vector) and get the more informative user preference vector. As for news representation, a modified transformer with category embedding is exploited to build news semantic representation. By comparing the hierarchical hash network with multiple news recommendation methods and conducting various experiments on the Microsoft News Dataset (MIND) validate the effectiveness of our approach on news recommendation.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2021EDP7034/_p
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@ARTICLE{e105-d_2_355,
author={Jianyong DUAN, Liangcai LI, Mei ZHANG, Hao WANG, },
journal={IEICE TRANSACTIONS on Information},
title={Hierarchical Preference Hash Network for News Recommendation},
year={2022},
volume={E105-D},
number={2},
pages={355-363},
abstract={Personalized news recommendation is becoming increasingly important for online news platforms to help users alleviate information overload and improve news reading experience. A key problem in news recommendation is learning accurate user representations to capture their interest. However, most existing news recommendation methods usually learn user representation only from their interacted historical news, while ignoring the clustering features among users. Here we proposed a hierarchical user preference hash network to enhance the representation of users' interest. In the hash part, a series of buckets are generated based on users' historical interactions. Users with similar preferences are assigned into the same buckets automatically. We also learn representations of users from their browsed news in history part. And then, a Route Attention is adopted to combine these two parts (history vector and hash vector) and get the more informative user preference vector. As for news representation, a modified transformer with category embedding is exploited to build news semantic representation. By comparing the hierarchical hash network with multiple news recommendation methods and conducting various experiments on the Microsoft News Dataset (MIND) validate the effectiveness of our approach on news recommendation.},
keywords={},
doi={10.1587/transinf.2021EDP7034},
ISSN={1745-1361},
month={February},}
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TY - JOUR
TI - Hierarchical Preference Hash Network for News Recommendation
T2 - IEICE TRANSACTIONS on Information
SP - 355
EP - 363
AU - Jianyong DUAN
AU - Liangcai LI
AU - Mei ZHANG
AU - Hao WANG
PY - 2022
DO - 10.1587/transinf.2021EDP7034
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E105-D
IS - 2
JA - IEICE TRANSACTIONS on Information
Y1 - February 2022
AB - Personalized news recommendation is becoming increasingly important for online news platforms to help users alleviate information overload and improve news reading experience. A key problem in news recommendation is learning accurate user representations to capture their interest. However, most existing news recommendation methods usually learn user representation only from their interacted historical news, while ignoring the clustering features among users. Here we proposed a hierarchical user preference hash network to enhance the representation of users' interest. In the hash part, a series of buckets are generated based on users' historical interactions. Users with similar preferences are assigned into the same buckets automatically. We also learn representations of users from their browsed news in history part. And then, a Route Attention is adopted to combine these two parts (history vector and hash vector) and get the more informative user preference vector. As for news representation, a modified transformer with category embedding is exploited to build news semantic representation. By comparing the hierarchical hash network with multiple news recommendation methods and conducting various experiments on the Microsoft News Dataset (MIND) validate the effectiveness of our approach on news recommendation.
ER -