Hongwei Wang
Xing Xie
ABSTRACT
To address the sparsity and cold start problem of collaborative filtering, researchers usually make use of side information, such as social networks or item attributes, to improve recommendation performance. This paper considers the knowledge graph as the source of side information. To address the limitations of existing embedding-based and path-based methods for knowledge-graph-aware recommendation, we propose RippleNet, an end-to-end framework that naturally incorporates the knowledge graph into recommender systems. Similar to actual ripples propagating on the water, RippleNet stimulates the propagation of user preferences over the set of knowledge entities by automatically and iteratively extending a user's potential interests along links in the knowledge graph. The multiple "ripples" activated by a user's historically clicked items are thus superposed to form the preference distribution of the user with respect to a candidate item, which could be used for predicting the final clicking probability. Through extensive experiments on real-world datasets, we demonstrate that RippleNet achieves substantial gains in a variety of scenarios, including movie, book and news recommendation, over several state-of-the-art baselines.
- Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014).Google Scholar
- Konstantin Bauman, Bing Liu, and Alexander Tuzhilin. 2017. Aspect based recommendations: Recommending items with the most valuable aspects based on user reviews. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 717--725. Google ScholarDigital Library
- 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 ScholarDigital Library
- Jingyuan Chen, Hanwang Zhang, Xiangnan He, Liqiang Nie, Wei Liu, and Tat-Seng Chua. 2017. Attentive collaborative filtering: Multimedia recommendation with item-and component-level attention. In SIGIR. ACM, 335--344. Google ScholarDigital Library
- Xu Chen, Hongteng Xu, Yongfeng Zhang, Jiaxi Tang, Yixin Cao, Zheng Qin, and Hongyuan Zha. 2018. Sequential Recommendation with User Memory Networks. In Proceedings of the 11th ACM International Conference on Web Search and Data Mining . Google ScholarDigital Library
- Heng-Tze Cheng, Levent Koc, Jeremiah Harmsen, Tal Shaked, Tushar Chandra, Hrishi Aradhye, Glen Anderson, Greg Corrado, Wei Chai, Mustafa Ispir, et almbox. 2016. Wide & deep learning for recommender systems. In Proceedings of the 1st Workshop on Deep Learning for Recommender Systems. ACM, 7--10. Google ScholarDigital Library
- Li Dong, Furu Wei, Ming Zhou, and Ke Xu. 2015. Question Answering over Freebase with Multi-Column Convolutional Neural Networks. In ACL . 260--269.Google Scholar
- Haoran Huang, Qi Zhang, and Xuanjing Huang. 2017. Mention Recommendation for Twitter with End-to-end Memory Network. In IJCAI . Google ScholarDigital Library
- Mohsen Jamali and Martin Ester. 2010. A matrix factorization technique with trust propagation for recommendation in social networks. In Proceedings of the 4th ACM conference on Recommender systems. ACM, 135--142. Google ScholarDigital Library
- Guoliang Ji, Shizhu He, Liheng Xu, Kang Liu, and Jun Zhao. 2015. Knowledge graph embedding via dynamic mapping matrix. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) , Vol. 1. 687--696.Google ScholarCross Ref
- Yehuda Koren. 2008. Factorization meets the neighborhood: a multifaceted collaborative filtering model. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 426--434. Google ScholarDigital Library
- Yehuda Koren, Robert Bell, and Chris Volinsky. 2009. Matrix factorization techniques for recommender systems. Computer , Vol. 42, 8 (2009). Google ScholarDigital Library
- Zheng Li, Yu Zhang, Ying Wei, Yuxiang Wu, and Qiang Yang. 2017. End-to-End Adversarial Memory Network for Cross-domain Sentiment Classification. In Proceedings of the 26th International Joint Conference on Artificial Intelligence . Google ScholarDigital Library
- Yankai Lin, Zhiyuan Liu, Maosong Sun, Yang Liu, and Xuan Zhu. 2015. Learning Entity and Relation Embeddings for Knowledge Graph Completion. In AAAI . 2181--2187. Google ScholarDigital Library
- Hanxiao Liu, Yuexin Wu, and Yiming Yang. 2017. Analogical Inference for Multi-Relational Embeddings. In Proceedings of the 34th International Conference on Machine Learning. 2168--2178.Google ScholarDigital Library
- Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013. Distributed representations of words and phrases and their compositionality. In Advances in Neural Information Processing Systems. 3111--3119. Google ScholarDigital Library
- Alexander Miller, Adam Fisch, Jesse Dodge, Amir-Hossein Karimi, Antoine Bordes, and Jason Weston. 2016. Key-value memory networks for directly reading documents. arXiv preprint arXiv:1606.03126 (2016).Google Scholar
- Volodymyr Mnih, Nicolas Heess, Alex Graves, et almbox. 2014. Recurrent models of visual attention. In Advances in Neural Information Processing Systems. 2204--2212. Google ScholarDigital Library
- Maximilian Nickel, Lorenzo Rosasco, Tomaso A Poggio, et almbox. 2016. Holographic Embeddings of Knowledge Graphs. In AAAI. 1955--1961. Google ScholarDigital Library
- Steffen Rendle. 2012. Factorization machines with libfm. ACM Transactions on Intelligent Systems and Technology (TIST) , Vol. 3, 3 (2012), 57. Google ScholarDigital Library
- Tim Rocktäschel, Sameer Singh, and Sebastian Riedel. 2015. Injecting logical background knowledge into embeddings for relation extraction. In Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies . 1119--1129.Google ScholarCross Ref
- Sungyong Seo, Jing Huang, Hao Yang, and Yan Liu. 2017. Interpretable convolutional neural networks with dual local and global attention for review rating prediction. In Proceedings of the Eleventh ACM Conference on Recommender Systems. ACM, 297--305. Google ScholarDigital Library
- Amit Sharma and Dan Cosley. 2013. Do social explanations work?: studying and modeling the effects of social explanations in recommender systems. In Proceedings of the 22nd international conference on World Wide Web. ACM, 1133--1144. Google ScholarDigital Library
- Sainbayar Sukhbaatar, Jason Weston, Rob Fergus, et almbox. 2015. End-to-end memory networks. In Advances in Neural Information Processing Systems. 2440--2448. Google ScholarDigital Library
- Yu Sun, Nicholas Jing Yuan, Xing Xie, Kieran McDonald, and Rui Zhang. 2017. Collaborative Intent Prediction with Real-Time Contextual Data. ACM Transactions on Information Systems , Vol. 35, 4 (2017), 30. Google ScholarDigital Library
- Kai Sheng Tai, Richard Socher, and Christopher D Manning. 2015. Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) , Vol. 1. 1556--1566.Google ScholarCross Ref
- Nava Tintarev and Judith Masthoff. 2007. A survey of explanations in recommender systems. In IEEE 23rd International Conference on Data Engineering Workshop. IEEE, 801--810. Google ScholarDigital Library
- Théo Trouillon, Johannes Welbl, Sebastian Riedel, Éric Gaussier, and Guillaume Bouchard. 2016. Complex embeddings for simple link prediction. In International Conference on Machine Learning . 2071--2080. Google ScholarDigital Library
- Jesse Vig, Shilad Sen, and John Riedl. 2009. Tagsplanations: explaining recommendations using tags. In Proceedings of the 14th international conference on Intelligent user interfaces. ACM, 47--56. Google ScholarDigital Library
- Hongwei Wang, Jia Wang, Jialin Wang, Miao Zhao, Weinan Zhang, Fuzheng Zhang, Xing Xie, and Minyi Guo. 2018a. Graphgan: Graph representation learning with generative adversarial nets. In AAAI . 2508--2515.Google Scholar
- Hongwei Wang, Jia Wang, Miao Zhao, Jiannong Cao, and Minyi Guo. 2017c. Joint Topic-Semantic-aware Social Recommendation for Online Voting. In Proceedings of the 2017 ACM Conference on Information and Knowledge Management. ACM, 347--356. Google ScholarDigital Library
- Hongwei Wang, Fuzheng Zhang, Min Hou, Xing Xie, Minyi Guo, and Qi Liu. 2018b. Shine: Signed heterogeneous information network embedding for sentiment link prediction. In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining. ACM, 592--600. Google ScholarDigital Library
- Hongwei Wang, Fuzheng Zhang, Xing Xie, and Minyi Guo. 2018c. DKN: Deep Knowledge-Aware Network for News Recommendation. In Proceedings of the 2018 World Wide Web Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 1835--1844. Google ScholarDigital Library
- Jin Wang, Zhongyuan Wang, Dawei Zhang, and Jun Yan. 2017b. Combining Knowledge with Deep Convolutional Neural Networks for Short Text Classification. In IJCAI . Google ScholarDigital Library
- Quan Wang, Zhendong Mao, Bin Wang, and Li Guo. 2017a. Knowledge graph embedding: A survey of approaches and applications. IEEE Transactions on Knowledge and Data Engineering , Vol. 29, 12 (2017), 2724--2743.Google ScholarCross Ref
- Xuejian Wang, Lantao Yu, Kan Ren, Guanyu Tao, Weinan Zhang, Yong Yu, and Jun Wang. 2017 d. Dynamic attention deep model for article recommendation by learning human editors' demonstration. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2051--2059. Google ScholarDigital Library
- Zhen Wang, Jianwen Zhang, Jianlin Feng, and Zheng Chen. 2014. Knowledge Graph Embedding by Translating on Hyperplanes. In AAAI . 1112--1119. Google ScholarDigital Library
- Jason Weston, Sumit Chopra, and Antoine Bordes. 2014. Memory networks. arXiv preprint arXiv:1410.3916 (2014).Google Scholar
- Ruobing Xie, Zhiyuan Liu, and Maosong Sun. 2016. Representation Learning of Knowledge Graphs with Hierarchical Types.. In IJCAI . 2965--2971. Google ScholarDigital Library
- Chang Xu, Yalong Bai, Jiang Bian, Bin Gao, Gang Wang, Xiaoguang Liu, and Tie-Yan Liu. 2014. Rc-net: A general framework for incorporating knowledge into word representations. In Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management. ACM, 1219--1228. Google ScholarDigital Library
- Bishan Yang, Wen-tau Yih, Xiaodong He, Jianfeng Gao, and Li Deng. 2015. Embedding entities and relations for learning and inference in knowledge bases. In Proceedings of the 3rd International Conference on Learning Representations .Google Scholar
- Xiao Yu, Xiang Ren, Yizhou Sun, Quanquan Gu, Bradley Sturt, Urvashi Khandelwal, Brandon Norick, and Jiawei Han. 2014. Personalized entity recommendation: A heterogeneous information network approach. In Proceedings of the 7th ACM International Conference on Web Search and Data Mining . 283--292. 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 . ACM, 353--362. Google ScholarDigital Library
- Yongfeng Zhang, Guokun Lai, Min Zhang, Yi Zhang, Yiqun Liu, and Shaoping Ma. 2014. Explicit factor models for explainable recommendation based on phrase-level sentiment analysis. In Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval. ACM, 83--92. Google ScholarDigital Library
- Huan Zhao, Quanming Yao, Jianda Li, Yangqiu Song, and Dik Lun Lee. 2017. Meta-graph based recommendation fusion over heterogeneous information networks. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 635--644. Google ScholarDigital Library
- Huaping Zhong, Jianwen Zhang, Zhen Wang, Hai Wan, and Zheng Chen. 2015. Aligning knowledge and text embeddings by entity descriptions. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing . 267--272.Google ScholarCross Ref
- Guorui Zhou, Chengru Song, Xiaoqiang Zhu, Xiao Ma, Yanghui Yan, Xingya Dai, Han Zhu, Junqi Jin, Han Li, and Kun Gai. 2017. Deep interest network for click-through rate prediction. arXiv preprint arXiv:1706.06978 (2017). Google ScholarDigital Library
Index Terms
- RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems
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