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Improving News Recommendation with Channel-Wise Dynamic Representations and Contrastive User Modeling

Published: 27 February 2023 Publication History

Abstract

News modeling and user modeling are the two core tasks of news recommendation. Accurate user representation and news representation can enable the recommendation system to provide users with precise recommendation services. Most existing methods use deep learning models such as CNN and Self-Attention to extract text features from news titles and abstracts to generate specific news vectors. However, the CNN-based methods have fixed parameters and cannot extract specific features for different input words, while the Self-Attention-based methods have high computational costs and are difficult to capture local features effectively. In our proposed method, we build a category-based dynamic component to generate suitable parameters for different inputs and extract local features from multiple perspectives. Meanwhile, users will mistakenly click on some news terms they are not interested in, so there will be some interaction noises in the datasets. In order to explore the critical user behaviors in user data and reduce the impact of noise data on user modeling, we adopt a frequency-aware contrastive learning method in user modeling. Experiments on real-world datasets verify the effectiveness of our proposed method.

Supplementary Material

MP4 File (WSDM23-fp0534.mp4)
News recommendation system can help people quickly get the news they are interested in. Most existing methods extract text features from news titles and abstracts to generate specific news vectors. However, these methods may lead to some certain problems. In this paper, we propose the MCCM model for news recommendation, which introduces channel-wise dynamic convolution and frequency-aware contrastive user modeling to enhance the representations of news and users. Experiments on a real-world dataset validate the effectiveness of our approach.

References

[1]
François Chollet. 2017. Xception: Deep Learning with Depthwise Separable Convolutions. In 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, July 21--26, 2017. IEEE Computer Society, 1800--1807. https://doi.org/10.1109/CVPR.2017.195
[2]
Suyu Ge, Chuhan Wu, Fangzhao Wu, Tao Qi, and Yongfeng Huang. 2020. Graph Enhanced Representation Learning for News Recommendation. In WWW '20: The Web Conference 2020, Taipei, Taiwan, April 20--24, 2020, Yennun Huang, Irwin King, Tie-Yan Liu, and Maarten van Steen (Eds.). ACM / IW3C2, 2863--2869. https://doi.org/10.1145/3366423.3380050
[3]
Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, and Xiuqiang He. 2017. DeepFM: A Factorization-Machine based Neural Network for CTR Prediction. In Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI 2017, Melbourne, Australia, August 19--25, 2017, Carles Sierra (Ed.). ijcai.org, 1725--1731. https://doi.org/10.24963/ijcai.2017/239
[4]
Linmei Hu, Siyong Xu, Chen Li, Cheng Yang, Chuan Shi, Nan Duan, Xing Xie, and Ming Zhou. 2020. Graph Neural News Recommendation with Unsupervised Preference Disentanglement. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020, Online, July 5--10, 2020, Dan Jurafsky, Joyce Chai, Natalie Schluter, and Joel R. Tetreault (Eds.). Association for Computational Linguistics, 4255--4264. https://doi.org/10.18653/v1/2020.aclmain. 392
[5]
Po-Sen Huang, Xiaodong He, Jianfeng Gao, Li Deng, Alex Acero, and Larry P. Heck. 2013. Learning deep structured semantic models for web search using clickthrough data. In 22nd ACM International Conference on Information and Knowledge Management, CIKM'13, San Francisco, CA, USA, October 27 - November 1, 2013, Qi He, Arun Iyengar, Wolfgang Nejdl, Jian Pei, and Rajeev Rastogi (Eds.). ACM, 2333--2338. https://doi.org/10.1145/2505515.2505665
[6]
Diederik P. Kingma and Jimmy Ba. 2015. Adam: A Method for Stochastic Optimization. In 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7--9, 2015, Conference Track Proceedings, Yoshua Bengio and Yann LeCun (Eds.). http://arxiv.org/abs/1412.6980
[7]
Jianxun Lian, Fuzheng Zhang, Xing Xie, and Guangzhong Sun. 2018. Towards Better Representation Learning for Personalized News Recommendation: a Multi-Channel Deep Fusion Approach. In Proceedings of the Twenty- Seventh International Joint Conference on Artificial Intelligence, IJCAI 2018, July 13--19, 2018, Stockholm, Sweden, Jérôme Lang (Ed.). ijcai.org, 3805--3811. https: //doi.org/10.24963/ijcai.2018/529
[8]
Danyang Liu, Jianxun Lian, Shiyin Wang, Ying Qiao, Jiun-Hung Chen, Guangzhong Sun, and Xing Xie. 2020. KRED: Knowledge-Aware Document Representation for News Recommendations. In RecSys 2020: Fourteenth ACM Conference on Recommender Systems, Virtual Event, Brazil, September 22--26, 2020, Rodrygo L. T. Santos, Leandro Balby Marinho, Elizabeth M. Daly, Li Chen, Kim Falk, Noam Koenigstein, and Edleno Silva de Moura (Eds.). ACM, 200--209. https://doi.org/10.1145/3383313.3412237
[9]
Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014. Glove: Global Vectors for Word Representation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014, October 25--29, 2014, Doha, Qatar, A meeting of SIGDAT, a Special Interest Group of the ACL, Alessandro Moschitti, Bo Pang, and Walter Daelemans (Eds.). ACL, 1532--1543. https://doi.org/10.3115/v1/d14--1162
[10]
Tao Qi, Fangzhao Wu, Chuhan Wu, and Yongfeng Huang. 2021. Personalized News Recommendation with Knowledge-aware Interactive Matching. In SIGIR '21: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual Event, Canada, July 11--15, 2021, Fernando Diaz, Chirag Shah, Torsten Suel, Pablo Castells, Rosie Jones, and Tetsuya Sakai (Eds.). ACM, 61--70. https://doi.org/10.1145/3404835.3462861
[11]
Steffen Rendle. 2012. Factorization Machines with libFM. ACM Trans. Intell. Syst. Technol. 3, 3 (2012), 57:1--57:22. https://doi.org/10.1145/2168752.2168771
[12]
Yu Tian, Yuhao Yang, Xudong Ren, Pengfei Wang, Fangzhao Wu, Qian Wang, and Chenliang Li. 2021. Joint Knowledge Pruning and Recurrent Graph Convolution for News Recommendation. In SIGIR '21: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual Event, Canada, July 11--15, 2021, Fernando Diaz, Chirag Shah, Torsten Suel, Pablo Castells, Rosie Jones, and Tetsuya Sakai (Eds.). ACM, 51--60. https: //doi.org/10.1145/3404835.3462912
[13]
Heyuan Wang, Fangzhao Wu, Zheng Liu, and Xing Xie. 2020. Fine-grained Interest Matching for Neural News Recommendation. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020, Online, July 5--10, 2020, Dan Jurafsky, Joyce Chai, Natalie Schluter, and Joel R. Tetreault (Eds.). Association for Computational Linguistics, 836--845. https://doi.org/10. 18653/v1/2020.acl-main.77
[14]
Hongwei Wang, Fuzheng Zhang, Xing Xie, and Minyi Guo. 2018. DKN: Deep Knowledge-Aware Network for News Recommendation. In Proceedings of the 2018 World Wide Web Conference on World Wide Web, WWW 2018, Lyon, France, April 23--27, 2018, Pierre-Antoine Champin, Fabien Gandon, Mounia Lalmas, and Panagiotis G. Ipeirotis (Eds.). ACM, 1835--1844. https://doi.org/10.1145/3178876. 3186175
[15]
Chuhan Wu, Fangzhao Wu, Mingxiao An, Jianqiang Huang, Yongfeng Huang, and Xing Xie. 2019. Neural News Recommendation with Attentive Multi-View Learning. In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI 2019, Macao, China, August 10--16, 2019, Sarit Kraus (Ed.). ijcai.org, 3863--3869. https://doi.org/10.24963/ijcai.2019/536
[16]
Chuhan Wu, Fangzhao Wu, Mingxiao An, Jianqiang Huang, Yongfeng Huang, and Xing Xie. 2019. NPA: Neural News Recommendation with Personalized Attention. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2019, Anchorage, AK, USA, August 4--8, 2019, Ankur Teredesai, Vipin Kumar, Ying Li, Rómer Rosales, Evimaria Terzi, and George Karypis (Eds.). ACM, 2576--2584. https://doi.org/10.1145/3292500.3330665
[17]
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 Conference of the Association for Computational Linguistics, ACL 2019, Florence, Italy, July 28- August 2, 2019, Volume 1: Long Papers, Anna Korhonen, David R. Traum, and Lluís Màrquez (Eds.). Association for Computational Linguistics, 1154--1159. https://doi.org/10.18653/v1/p19--1110
[18]
ChuhanWu, FangzhaoWu, Suyu Ge, Tao Qi, Yongfeng Huang, and Xing Xie. 2019. Neural News Recommendation with Multi-Head Self-Attention. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, EMNLP-IJCNLP 2019, Hong Kong, China, November 3--7, 2019, Kentaro Inui, Jing Jiang, Vincent Ng, and Xiaojun Wan (Eds.). Association for Computational Linguistics, 6388--6393. https://doi.org/10.18653/v1/D19--1671
[19]
Chuhan Wu, Fangzhao Wu, Yang Yu, Tao Qi, Yongfeng Huang, and Qi Liu. 2021. NewsBERT: Distilling Pre-trained Language Model for Intelligent News Application. In Findings of the Association for Computational Linguistics: EMNLP 2021, Virtual Event / Punta Cana, Dominican Republic, 16--20 November, 2021, Marie- Francine Moens, Xuanjing Huang, Lucia Specia, and Scott Wen-tau Yih (Eds.). Association for Computational Linguistics, 3285--3295. https://doi.org/10.18653/ v1/2021.findings-emnlp.280
[20]
Fangzhao Wu, Ying Qiao, Jiun-Hung Chen, Chuhan Wu, Tao Qi, Jianxun Lian, Danyang Liu, Xing Xie, Jianfeng Gao, Winnie Wu, and Ming Zhou. 2020. MIND: A Large-scale Dataset for News Recommendation. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020, Online, July 5--10, 2020, Dan Jurafsky, Joyce Chai, Natalie Schluter, and Joel R. Tetreault (Eds.). Association for Computational Linguistics, 3597--3606. https://doi.org/10. 18653/v1/2020.acl-main.331
[21]
Qi Zhang, Jingjie Li, Qinglin Jia, Chuyuan Wang, Jieming Zhu, Zhaowei Wang, and Xiuqiang He. 2021. UNBERT: User-News Matching BERT for News Recommendation. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI 2021, Virtual Event / Montreal, Canada, 19--27 August 2021, Zhi-Hua Zhou (Ed.). ijcai.org, 3356--3362. https://doi.org/10.24963/ijcai.2021/462
[22]
Qiannan Zhu, Xiaofei Zhou, Zeliang Song, Jianlong Tan, and Li Guo. 2019. DAN: Deep Attention Neural Network for News Recommendation. In The Thirty- Third AAAI Conference on Artificial Intelligence, AAAI 2019, The Thirty-First Innovative Applications of Artificial Intelligence Conference, IAAI 2019, The Ninth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019, Honolulu, Hawaii, USA, January 27 - February 1, 2019. AAAI Press, 5973--5980. https://doi.org/10.1609/aaai.v33i01.33015973

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  • (2025)What if User Preferences Shifts: Causal Disentanglement for News RecommendationDatabase Systems for Advanced Applications10.1007/978-981-97-5779-4_36(496-506)Online publication date: 11-Jan-2025
  • (2025)Multi-Aspect Matching between Disentangled Representations of User Interests and Content for News RecommendationDatabase Systems for Advanced Applications10.1007/978-981-97-5779-4_29(426-435)Online publication date: 11-Jan-2025
  • (2024)NQNR: News Recommendation Method Based on News Quality-Aware Modeling2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC54092.2024.10831543(1779-1786)Online publication date: 6-Oct-2024
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      cover image ACM Conferences
      WSDM '23: Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining
      February 2023
      1345 pages
      ISBN:9781450394079
      DOI:10.1145/3539597
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      Published: 27 February 2023

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      1. contrastive learning
      2. dynamic
      3. news recommendation

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      • (2025)What if User Preferences Shifts: Causal Disentanglement for News RecommendationDatabase Systems for Advanced Applications10.1007/978-981-97-5779-4_36(496-506)Online publication date: 11-Jan-2025
      • (2025)Multi-Aspect Matching between Disentangled Representations of User Interests and Content for News RecommendationDatabase Systems for Advanced Applications10.1007/978-981-97-5779-4_29(426-435)Online publication date: 11-Jan-2025
      • (2024)NQNR: News Recommendation Method Based on News Quality-Aware Modeling2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC54092.2024.10831543(1779-1786)Online publication date: 6-Oct-2024
      • (2024)HGTA: News Recommendation Based on Hierarchical Granular Semantic Embeddings and Threshold Attention2024 27th International Conference on Computer Supported Cooperative Work in Design (CSCWD)10.1109/CSCWD61410.2024.10580511(1937-1943)Online publication date: 8-May-2024
      • (2024)Dual-view hypergraph attention network for news recommendationEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.108256133(108256)Online publication date: Jul-2024
      • (2024)A contrastive news recommendation framework based on curriculum learningUser Modeling and User-Adapted Interaction10.1007/s11257-024-09422-035:1Online publication date: 28-Dec-2024
      • (2024)Don’t Click the Bait: Title Debiasing News Recommendation via Cross-Field Contrastive LearningNatural Language Processing and Chinese Computing10.1007/978-981-97-9440-9_18(224-236)Online publication date: 1-Nov-2024
      • (2023)Contrastive Self-supervised Learning in Recommender Systems: A SurveyACM Transactions on Information Systems10.1145/362715842:2(1-39)Online publication date: 8-Nov-2023
      • (2023)A Survey of Personalized News RecommendationData Science and Engineering10.1007/s41019-023-00228-58:4(396-416)Online publication date: 2-Sep-2023
      • (2023)News Recommendation via Jointly Modeling Event Matching and Style MatchingMachine Learning and Knowledge Discovery in Databases: Research Track10.1007/978-3-031-43421-1_24(404-419)Online publication date: 18-Sep-2023

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