Heng-Shiou Sheu
ABSTRACT
Online news recommender systems aim to make personalized recommendations according to user preferences, which require modeling users’ short-term reading interest. However, due to the limited logged user interactions in practice, news recommendation at session-level becomes very challenging. Existing methods on session-based news recommendation mainly focus on extracting features from news articles and sequential user-item interactions, but they usually ignore the semantic-level structural information among news articles and do not explore external knowledge sources. In this paper, we propose a novel Context-Aware Graph Embedding (CAGE) framework for session-based news recommendation, which builds an auxiliary knowledge graph to enrich the semantic meaning of entities involved in articles, and further refines the article embeddings by graph convolutional networks. Experimental results on a real-world news dataset demonstrate the effectiveness of our method compared with the state-of-the-art methods on session-based news recommendation.
- Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko. 2013. Translating embeddings for modeling multi-relational data. In Advances in neural information processing systems. 2787–2795.Google Scholar
- Abhinandan S Das, Mayur Datar, Ashutosh Garg, and Shyam Rajaram. 2007. Google news personalization: scalable online collaborative filtering. In Proceedings of the 16th international conference on World Wide Web. 271–280.Google ScholarDigital Library
- Gabriel de Souza Pereira Moreira, Felipe Ferreira, and Adilson Marques da Cunha. 2018. News session-based recommendations using deep neural networks. In The 3rd Workshop on Deep Learning for Recommender Systems. 15–23.Google ScholarDigital Library
- Jon Atle Gulla, Lemei Zhang, Peng Liu, Özlem Özgöbek, and Xiaomeng Su. 2017. The Adressa dataset for news recommendation. In Proceedings of the international conference on web intelligence. 1042–1048.Google ScholarDigital Library
- Balázs Hidasi and Alexandros Karatzoglou. 2018. Recurrent neural networks with top-k gains for session-based recommendations. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management. 843–852.Google ScholarDigital Library
- Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. 2015. Session-based recommendations with recurrent neural networks. arXiv preprint arXiv:1511.06939(2015).Google Scholar
- Jin Huang, Wayne Xin Zhao, Hongjian Dou, Ji-Rong Wen, and Edward Y Chang. 2018. Improving sequential recommendation with knowledge-enhanced memory networks. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval. 505–514.Google ScholarDigital Library
- 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
- Guoliang Ji, Shizhu He, Liheng Xu, Kang Liu, and Jun Zhao. 2015. Knowledge Graph Embedding via Dynamic Mapping Matrix. In ACL. 687–696.Google Scholar
- Xiaodong Jiang, Pengsheng Ji, and Sheng Li. 2019. CensNet: Convolution with Edge-Node Switching in Graph Neural Networks.. In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence. 2656–2662.Google ScholarCross Ref
- Michael Jugovac, Dietmar Jannach, and Mozhgan Karimi. 2018. Streamingrec: a framework for benchmarking stream-based news recommenders. In Proceedings of the 12th ACM Conference on Recommender Systems. 269–273.Google ScholarDigital Library
- Iman Kamehkhosh, Dietmar Jannach, and Malte Ludewig. 2017. A Comparison of Frequent Pattern Techniques and a Deep Learning Method for Session-Based Recommendation.. In RecTemp@ RecSys. 50–56.Google Scholar
- Donghyun Kim, Sungchul Kim, Handong Zhao, Sheng Li, Ryan A Rossi, and Eunyee Koh. 2019. Domain switch-aware holistic recurrent neural network for modeling multi-domain user behavior. In Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining. 663–671.Google ScholarDigital Library
- Thomas N Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907(2016).Google Scholar
- Sheng Li, Jaya Kawale, and Yun Fu. 2015. Deep collaborative filtering via marginalized denoising auto-encoder. In Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. 811–820.Google ScholarDigital Library
- Sheng Li, Jaya Kawale, and Yun Fu. 2015. Predicting user behavior in display advertising via dynamic collective matrix factorization. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval. 875–878.Google ScholarDigital Library
- Sheng Li and Handong Zhao. 2020. A Survey on Representation Learning for User Modeling. In Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence. 4997–5003.Google ScholarCross Ref
- Qiao Liu, Yifu Zeng, Refuoe Mokhosi, and Haibin Zhang. 2018. STAMP: short-term attention/memory priority model for session-based recommendation. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 1831–1839.Google ScholarDigital Library
- Malte Ludewig and Dietmar Jannach. 2018. Evaluation of session-based recommendation algorithms. User Modeling and User-Adapted Interaction 28, 4-5 (2018), 331–390.Google ScholarDigital Library
- Gabriel de Souza Pereira Moreira, Dietmar Jannach, and Adilson Marques da Cunha. 2019. Contextual hybrid session-based news recommendation with recurrent neural networks. arXiv preprint arXiv:1904.10367(2019).Google Scholar
- Zhiqing Sun, Zhi-Hong Deng, Jian-Yun Nie, and Jian Tang. 2019. Rotate: Knowledge graph embedding by relational rotation in complex space. arXiv preprint arXiv:1902.10197(2019).Google Scholar
- Yong Kiam Tan, Xinxing Xu, and Yong Liu. 2016. Improved recurrent neural networks for session-based recommendations. In Proceedings of the 1st Workshop on Deep Learning for Recommender Systems. 17–22.Google ScholarDigital Library
- Zhiqiang Tao, Sheng Li, Zhaowen Wang, Chen Fang, Longqi Yang, Handong Zhao, and Yun Fu. 2019. Log2Intent: Towards interpretable user modeling via recurrent semantics memory unit. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 1055–1063.Google ScholarDigital Library
- Saúl Vargas and Pablo Castells. 2011. Rank and Relevance in Novelty and Diversity Metrics for Recommender Systems. In Proceedings of the Fifth ACM Conference on Recommender Systems (Chicago, Illinois, USA) (RecSys ’11). Association for Computing Machinery, New York, NY, USA, 109–116. https://doi.org/10.1145/2043932.2043955Google ScholarDigital Library
- Denny Vrandečić and Markus Krötzsch. 2014. Wikidata: a free collaborative knowledgebase. Commun. ACM 57, 10 (2014), 78–85.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 (Lyon, France) (WWW ’18). International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE, 1835–1844.Google ScholarDigital Library
- Xiang Wang, Xiangnan He, Yixin Cao, Meng Liu, and Tat-Seng Chua. 2019. Kgat: Knowledge graph attention network for recommendation. In SIGKDD. 950–958.Google ScholarDigital Library
- Xiang Wang, Dingxian Wang, Canran Xu, Xiangnan He, Yixin Cao, and Tat-Seng Chua. 2019. Explainable reasoning over knowledge graphs for recommendation. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 5329–5336.Google ScholarDigital Library
- Zhen Wang, Jianwen Zhang, Jianlin Feng, and Zheng Chen. 2014. Knowledge graph embedding by translating on hyperplanes. In Twenty-Eighth AAAI conference on artificial intelligence.Google ScholarDigital Library
- Shu Wu, Yuyuan Tang, Yanqiao Zhu, Liang Wang, Xing Xie, and Tieniu Tan. 2019. Session-based recommendation with graph neural networks. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 346–353.Google ScholarDigital Library
- Fuzheng Zhang, Nicholas Jing Yuan, Defu Lian, Xing Xie, and Wei-Ying Ma. 2016. Collaborative knowledge base embedding for recommender systems. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. 353–362.Google ScholarDigital Library
Index Terms
- Context-aware Graph Embedding for Session-based News Recommendation
Index terms have been assigned to the content through auto-classification.
Recommendations
Graph Embedding Based Recommendation Techniques on the Knowledge Graph
UMAP '17: Adjunct Publication of the 25th Conference on User Modeling, Adaptation and PersonalizationThis paper presents a novel, graph embedding based recommendation technique. The method operates on the knowledge graph, an information representation technique alloying content-based and collaborative information. To generate recommendations, a two ...
Item sequential recommendation based on graph embedding model
AbstractTo solve the problem of low recommendation accuracy, caused by the low accuracy of item feature representation and data sparsity in item sequential recommendation, this paper proposes an item sequential recommendation method based on a graph ...
Context-aware item attraction model for session-based recommendation
Highlights- Propose an item attraction model for session-based recommendation.
- Convert ...
AbstractSession-based recommendation uses existing items in users’ interaction sessions to predict the next items with which users will interact. The existing items in sessions usually have different degrees of relevance with each other, and ...
Comments