Explainable Recommendation and Search refers to the (personalized) recommendation and search algorithms that not only provide the user with the search and recommendation results, but also let the user know why such results are provided, i.e., they try to address the problem of “why” in recommendation and search systems. Explainable recommendation and search algorithms devise interpretable models and generates intuitive explanations for users. The explanations can be provided in various forms, such as word clouds, sentences, visual images, or diagrams, etc, which help to improve the effectiveness, efficiency, persuasiveness, and user satisfaction of recommendation and search systems.

Related Publications:

[1] Yongfeng Zhang and Xu Chen. Explainable Recommendation: A Survey and New Perspectives. arXiv Preprint 2018. arXiv:1804.11192.

[2] Yongfeng Zhang, Guokun Lai, Min Zhang, Yi Zhang, Yiqun Liu and Shaoping Ma. Explicit Factor Models for Explainable Recommendation based on Phrase-level Sentiment Analysis. In Proceedings of the 37th Annual International ACM SIGIR Conference on Research and Development on Information Retrieval (SIGIR 2014), July 6 – 11, 2014, Gold Coast, Australia.

[3] Xu Chen, Yongfeng Zhang, Hongteng Xu, Yixin Cao, Zheng Qin, and Hongyuan Zha. Visually Explainable Recommendation. Preprint.

[4] Xu Chen, Yongfeng Zhang, Zheng Qin. Dynamic Explainable Recommendation based on Neural Attentive Models. In Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI 2019), January 27 – February 1, 2019, Hawaii, USA.

[5] Xu Chen, Zheng Qin, Yongfeng Zhang, and Tao Xu. Learning to Rank Features for Recommendation over Multiple Categories. In Proceedings of the 39th Annual International ACM SIGIR Conference on Research and Development on Information Retrieval (SIGIR 2016), July 17 – 21, 2016, Pisa, Italy.

[6] Xu Chen, Hongteng Xu, Yongfeng Zhang, Yixin Cao, Hongyuan Zha, Zheng Qin and Jiaxi Tang. Sequential Recommendation with User Memory Networks. In Proceedings of the 11th ACM International Conference on Web Search and Data Mining (WSDM 2018), February 5 – 9, 2018, Los Angeles, California, USA.

[7] Yongfeng Zhang. Incorporating Phrase-level Sentiment Analysis on Textual Reviews for Personalized Recommendation. In Proceedings of the 8th International Conference on Web Search and Data Mining (WSDM 2015), Feb. 2 – 6, 2015, Shanghai, China.

[8] Yongfeng Zhang. Browser-Oriented Universal Cross-Site Recommendation and Explanation based on User Browsing Logs. In Proceedings of the 8th ACM Conference Series on Recommender Systems (RecSys 2014), Oct. 6 – 10, 2014, Foster City, Silicon Valley, USA.

[9] Yongfeng Zhang, Min Zhang, Yi Zhang, Guokun Lai, Yiqun Liu, Honghui Zhang, Shaoping Ma. Daily-Aware Personalized Recommendation based on Feature-Level Time Series Analysis. In Proceedings of the 24th International World Wide Web Conference (WWW 2015), May 18 – 22, 2015, Florence, Italy.