Xuanyu Zhang
ABSTRACT
News recommendation is very crucial for online news services to improve user experience and alleviate information overload. Precisely learning representations of news and users is the core problem in news recommendation. Existing models usually focus on implicit text information to learn corresponding representations, which may be insufficient for modeling user interests. Even if entity information is considered from external knowledge, it may still not be used explicitly and effectively for user modeling. In this paper, we propose a novel news recommendation approach, which combine explicit entity graph with implicit text information. The entity graph consists of two types of nodes and three kinds of edges, which represent chronological order, related and affiliation relationship. Then graph neural network is utilized for reasoning on these nodes. Extensive experiments on a real-world dataset, Microsoft News Dataset (MIND), validate the effectiveness of our proposed approach.
- Mingxiao An, Fangzhao Wu, Chuhan Wu, Kun Zhang, Zheng Liu, and Xing Xie. 2019. Neural News Recommendation with Long- and Short-term User Representations. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Florence, Italy.Google Scholar
Cross Ref
- Trapit Bansal, Mrinal Das, and Chiranjib Bhattacharyya. 2015. Content driven user profiling for comment-worthy recommendations of news and blog articles. In Proceedings of the 9th ACM Conference on Recommender Systems. 195–202.Google ScholarDigital Library
- Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Identity mappings in deep residual networks. In European conference on computer vision. Springer, 630–645.Google ScholarCross Ref
- Linmei Hu, Chen Li, Chuan Shi, Cheng Yang, and Chao Shao. 2020. Graph neural news recommendation with long-term and short-term interest modeling. Information Processing & Management 57, 2 (2020), 102142.Google ScholarDigital Library
- 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. Association for Computational Linguistics, Online.Google ScholarCross Ref
- Po-Sen Huang, Xiaodong He, Jianfeng Gao, Li Deng, Alex Acero, and Larry Heck. 2013. Learning deep structured semantic models for web search using clickthrough data. In Proceedings of the 22nd ACM international conference on Information & Knowledge Management. 2333–2338.Google ScholarDigital Library
- Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980(2014).Google Scholar
- 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. 3805–3811.Google ScholarCross Ref
- 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 Fourteenth ACM Conference on Recommender Systems. 200–209.Google Scholar
- Jiahui Liu, Peter Dolan, and Elin Rønby Pedersen. 2010. Personalized news recommendation based on click behavior. In Proceedings of the 15th international conference on Intelligent user interfaces. 31–40.Google ScholarDigital Library
- Shumpei Okura, Yukihiro Tagami, Shingo Ono, and Akira Tajima. 2017. Embedding-Based News Recommendation for Millions of Users. Association for Computing Machinery.Google Scholar
- 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. 1532–1543.Google ScholarCross Ref
- Jeong-Woo Son, A-Yeong Kim, and Seong-Bae Park. 2013. A location-based news article recommendation with explicit localized semantic analysis. In Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval. 293–302.Google ScholarDigital Library
- Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014. Dropout: a simple way to prevent neural networks from overfitting. The journal of machine learning research 15, 1 (2014), 1929–1958.Google ScholarDigital Library
- 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. 1835–1844.Google ScholarDigital Library
- 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. International Joint Conferences on Artificial Intelligence Organization, 3863–3869.Google ScholarCross Ref
- 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. 2576–2584.Google ScholarDigital Library
- Chuhan Wu, Fangzhao Wu, 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. Association for Computational Linguistics, Hong Kong, China.Google Scholar
- 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. Association for Computational Linguistics, Online.Google ScholarCross Ref
- Shuangfei Zhai, Keng-hao Chang, Ruofei Zhang, and Zhongfei Mark Zhang. 2016. Deepintent: Learning attentions for online advertising with recurrent neural networks. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. 1295–1304.Google ScholarDigital Library
- Xuanyu Zhang. 2020. CFGNN: Cross Flow Graph Neural Networks for Question Answering on Complex Tables. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 9596–9603.Google ScholarCross Ref
- Combining Explicit Entity Graph with Implicit Text Information for News Recommendation
Recommendations
Graph neural news recommendation with long-term and short-term interest modeling
Highlights- We propose a novel graph neural news recommendation model GNewsRec with long-term and short-term user interest modeling.
AbstractWith the information explosion of news articles, personalized news recommendation has become important for users to quickly find news that they are interested in. Existing methods on news recommendation mainly include collaborative ...
Personalized news recommendation: a review and an experimental investigation
Special issue on Community Analysis and Information RecommendationOnline news articles, as a new format of press releases, have sprung up on the Internet. With its convenience and recency, more and more people prefer to read news online instead of reading the paper-format press releases. However, a gigantic amount of ...
Personalized news recommendation based on articles chain building
News personalized recommendation has long been a favorite research in recommender. Previous methods strive to satisfy the users by constructing the users' preference profiles. Traditionally, most of recent researches use users' reading history (content ...
Comments