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Neural News Recommendation with Topic-Aware News Representation

Chuhan Wu, Fangzhao Wu, Mingxiao An, Yongfeng Huang, Xing Xie
2019 Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics  
News recommendation can help users find interested news and alleviate information overload.  ...  Acknowledgments The authors would like to thank Microsoft News for providing technical support and data in the experiments, and Jiun-Hung Chen (Microsoft News) and Ying Qiao (Microsoft News) for their  ...  Conclusion In this paper, we propose a neural news recommendation approach with topic-aware news representations.  ... 
doi:10.18653/v1/p19-1110 dblp:conf/acl/WuWAHX19 fatcat:zuyjmouzuzc2hpx6a5qxhrpa64

Personalized News Recommendation: Methods and Challenges [article]

Chuhan Wu, Fangzhao Wu, Yongfeng Huang, Xing Xie
2022 arXiv   pre-print
To help researchers master the advances in personalized news recommendation over the past years, in this paper we present a comprehensive overview of personalized news recommendation.  ...  Instead of following the conventional taxonomy of news recommendation methods, in this paper we propose a novel perspective to understand personalized news recommendation based on its core problems and  ...  It predicts the topic of news based on texts and concepts, and used the predicted topic to enrich the knowledge graph and learn topic enriched knowledge representations of news with graph neural networks  ... 
arXiv:2106.08934v3 fatcat:iagqsw73hrehxaxpvpydvtr26m

Personalized News Recommendation: Methods and Challenges

Chuhan Wu, Fangzhao Wu, Yongfeng Huang, Xing Xie
2022 ACM Transactions on Information Systems  
To help researchers master the advances in personalized news recommendation, in this paper we present a comprehensive overview of personalized news recommendation.  ...  Personalized news recommendation is important for users to find interested news information and alleviate information overload.  ...  It predicts the topic of news based on texts and concepts, and uses the predicted topic to enrich the knowledge graph and learn topic enriched knowledge representations of news with graph neural networks  ... 
doi:10.1145/3530257 fatcat:xzghh6cut5ahhgxz4mkzgy74ja

Neural News Recommendation with Long- and Short-term User Representations

Mingxiao An, Fangzhao Wu, Chuhan Wu, Kun Zhang, Zheng Liu, Xing Xie
2019 Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics  
Personalized news recommendation is important to help users find their interested news and improve reading experience.  ...  Acknowledgement The authors would like to thank Microsoft News for providing technical support and data in the experiments, and Jiun-Hung Chen (Microsoft News) and Ying Qiao (Microsoft News) for their  ...  In this paper, we propose a neural news recommendation approach with both long-and shortterm user representations (LSTUR).  ... 
doi:10.18653/v1/p19-1033 dblp:conf/acl/AnWWZLX19 fatcat:gj3pkktkvrfung75bvypee3ohi

Aspect-driven User Preference and News Representation Learning for News Recommendation [article]

Rongyao Wang, Wenpeng Lu, Shoujin Wang, Xueping Peng, Hao Wu, Qian Zhang
2022 arXiv   pre-print
Most of existing news recommender systems usually learn topic-level representations of users and news for recommendation, and neglect to learn more informative aspect-level features of users and news for  ...  Aiming at addressing this deficiency, we propose a novel Aspect-driven News Recommender System (ANRS) built on aspect-level user preference and news representation learning.  ...  For example, TANR [8] is a neural topic-aware news recommender system, which trains the news encoder with an auxiliary topic classification task to learn accurate news representations.  ... 
arXiv:2110.05792v2 fatcat:wkd45dtj45ge5dodb2cxnggcdu

SentiRec: Sentiment Diversity-aware Neural News Recommendation

Chuhan Wu, Fangzhao Wu, Tao Qi, Yongfeng Huang
2020 International Joint Conference on Natural Language Processing  
In this paper, we propose a sentiment diversity-aware neural news recommendation approach, which can recommend news with more diverse sentiment.  ...  In our approach, we propose a sentiment-aware news encoder, which is jointly trained with an auxiliary sentiment prediction task, to learn sentiment-aware news representations.  ...  news representations via knowledgeaware CNN networks and learns user representations with a candidate-aware attention network. (4) Conv3D (Khattar et al., 2018) , a neural news recommendation method which  ... 
dblp:conf/ijcnlp/WuWQH20 fatcat:m3hm43ejubfmfgmrcwrdn435fe

The Graph-Based Behavior-Aware Recommendation for Interactive News [article]

Mingyuan Ma, Sen Na, Hongyu Wang, Congzhou Chen, Jin Xu
2021 arXiv   pre-print
Second, we apply DeepWalk on the behavior graph to obtain entity semantics, then build a graph-based convolutional neural network called G-CNN to learn news representations, and an attention-based LSTM  ...  Interactive news recommendation has been launched and attracted much attention recently.  ...  Further, the user representation together with the news representation form the feature learning part of our recommendation system.  ... 
arXiv:1812.00002v2 fatcat:kwhugfvc4faapkesblicj5xfhe

A Survey of Deep Learning Approaches for Recommendation Systems

Jun Yi Liu
2018 Journal of Physics, Conference Series  
As deep learning develops, the application of deep neural network in related research is increasingly prevalent.  ...  The detailed description of each recommendation system is explained, and the related datasets are briefly introduced.  ...  The Deep Learning Models Applications Considering the frequent changes of hot news topics, Oh et al. introduced a personalized news recommender system.  ... 
doi:10.1088/1742-6596/1087/6/062022 fatcat:2mep7pcyvrdvldwq7z3ts6w6gm

Towards Comprehensive Recommender Systems: Time-Aware Unified Recommendations Based on Listwise Ranking of Implicit Cross-Network Data

Dilruk Perera, Roger Zimmermann
2020 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
Despite the effectiveness of existing recommender systems, we find two major limitations that reduce their overall performance: (1) inability to provide timely recommendations for both new and existing  ...  Therefore, we propose a novel deep learning based unified cross-network solution to mitigate cold-start and data sparsity issues and provide timely recommendations for new and existing users.  ...  Thus to the best of our knowledge, we proposed the first time aware unified cross-network recommender solution with a generic listwise loss function.  ... 
doi:10.1609/aaai.v34i01.5350 fatcat:vm4fsg5fsbhezabexk7wetqtpq

Towards Comprehensive Recommender Systems: Time-Aware UnifiedcRecommendations Based on Listwise Ranking of Implicit Cross-Network Data [article]

Dilruk Perera, Roger Zimmermann
2020 arXiv   pre-print
Despite the effectiveness of existing recommender systems, we find two major limitations that reduce their overall performance: (1) inability to provide timely recommendations for both new and existing  ...  Therefore, we propose a novel deep learning based unified cross-network solution to mitigate cold-start and data sparsity issues and provide timely recommendations for new and existing users.Furthermore  ...  Thus to the best of our knowledge, we proposed the first time aware unified cross-network recommender solution with a generic listwise loss function.  ... 
arXiv:2008.13516v1 fatcat:qw27hdqasjaatnqyw5ve5g6rje

SAM: A Self-adaptive Attention Module for Context-Aware Recommendation System [article]

Jiabin Liu, Zheng Wei, Zhengpin Li, Xiaojun Mao, Jian Wang, Zhongyu Wei, Qi Zhang
2021 arXiv   pre-print
Recently, textual information has been proved to play a positive role in recommendation systems.  ...  This module can be embedded into recommendation systems that contain learning components of contextual information.  ...  [8] presented an innovative recommendation model, which utilizes the attention-based recurrent neural networks to extract topical information from review documents.  ... 
arXiv:2110.00452v3 fatcat:oky6uruptrbppdo2euklrwct3a

Neural News Recommendation with Collaborative News Encoding and Structural User Encoding [article]

Zhiming Mao, Xingshan Zeng, Kam-Fai Wong
2021 arXiv   pre-print
CNE equipped with bidirectional LSTMs encodes news title and content collaboratively with cross-selection and cross-attention modules to learn semantic-interactive news representations.  ...  In this work, we propose a news recommendation framework consisting of collaborative news encoding (CNE) and structural user encoding (SUE) to enhance news and user representation learning.  ...  Neural News Recommendation Methods.  ... 
arXiv:2109.00750v2 fatcat:t6c3g65jpbdzvmfmi7nhjwdgpe

A^3NCF: An Adaptive Aspect Attention Model for Rating Prediction

Zhiyong Cheng, Ying Ding, Xiangnan He, Lei Zhu, Xuemeng Song, Mohan Kankanhalli
2018 Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence  
Specifically, we design a new topic model to extract user preferences and item characteristics from review texts.  ...  They are then used to 1) guide the representation learning of users and items, and 2) capture a user's special attention on each aspect of the targeted item with an attention network.  ...  In particular, a new topic model is developed to extract both user and item features from reviews to guide the aspect-aware representation learning. • We introduce an attention network to capture the varying  ... 
doi:10.24963/ijcai.2018/521 dblp:conf/ijcai/ChengD0ZSK18 fatcat:ykagpwrxjvgtlmwovhmv5ehcs4

Neural News Recommendation with Negative Feedback [article]

Chuhan Wu, Fangzhao Wu, Yongfeng Huang, Xing Xie
2021 arXiv   pre-print
In this paper, we propose a neural news recommendation approach which can incorporate the implicit negative user feedback.  ...  News recommendation is important for online news services. Precise user interest modeling is critical for personalized news recommendation.  ...  -DKN [26], a neural news recommendation method which learns news representations via knowledge-aware CNNs and learns user representations via an attention network based on the relevance between clicked  ... 
arXiv:2101.04328v1 fatcat:oilphr4mrrdghi6ssi67e3d2qa

Neural news recommendation with negative feedback

Chuhan Wu, Fangzhao Wu, Yongfeng Huang, Xing Xie
2020 CCF Transactions on Pervasive Computing and Interaction  
In this paper, we propose a neural news recommendation approach which can incorporate the implicit negative user feedback.  ...  News recommendation is important for online news services. Precise user interest modeling is critical for personalized news recommendation.  ...  Acknowledgements The authors would like to thank Microsoft News for providing technical support and data.  ... 
doi:10.1007/s42486-020-00044-0 fatcat:ox4j56jzl5hbvlccnpyzfg6uvu
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