{"type":"ScholarlyArticle","title":"Towards Explainable Conversational Recommendation","authors":[{"name":"Zhongxia Chen","slug":"zhongxia-chen","given":"Zhongxia","family":"Chen","affiliation":"School of Computer Science and Technology , University of Science and Technology of China; Microsoft Research Asia"},{"name":"Xiting Wang","slug":"xiting-wang","given":"Xiting","family":"Wang","affiliation":"Microsoft Research Asia"},{"name":"Xing Xie","slug":"xing-xie","given":"Xing","family":"Xie","affiliation":"Microsoft Research Asia"},{"name":"Mehul Parsana","slug":"mehul-parsana","given":"Mehul","family":"Parsana","affiliation":"Microsoft Bing Ads"},{"name":"Akshay Soni","slug":"akshay-soni","given":"Akshay","family":"Soni","affiliation":"Microsoft Bing Ads"},{"name":"Xiang Ao","slug":"xiang-ao","given":"Xiang","family":"Ao","affiliation":"Institute of Computing Technology , Chinese Academy of Sciences"},{"name":"Enhong Chen","slug":"enhong-chen","given":"Enhong","family":"Chen","affiliation":"School of Computer Science and Technology , University of Science and Technology of China"}],"abstract":"Recent studies have shown that both accuracy and explainability are important for recommendation. In this paper, we introduce explainable conversational recommendation, which enables incremental improvement of both recommendation accuracy and explanation quality through multi-turn user-model conversation. We show how the problem can be formulated, and design an incremental multi-task learning framework that enables tight collaboration between recommendation prediction, explanation generation, and user feedback integration. We also propose a multi-view feedback integration method to enable effective incremental model update. Empirical results demonstrate that our model not only consistently improves the recommendation accuracy but also generates explanations that fit user interests reflected in the feedbacks.","doi":"10.24963/ijcai.2020/414","url":"https://ijcai.org/proceedings/2020/414","pdf":"https://ijcai.org/proceedings/2020/0414.pdf","markdown":"https://ijcai.org/proceedings/2020/414.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/414","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Machine Learning","track":"Main track","number":414,"pages":{"first":2994,"last":3000},"keywords":["Machine Learning: Explainable Machine Learning","Natural Language Processing: Natural Language Generation","Machine Learning: Recommender Systems"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/25379/file_25379.m3u8","length_sec":238,"provider":"hls"}],"references":[{"position":1,"raw":"[Bi et al., 2019] Keping Bi, Qingyao Ai, Yongfeng Zhang, and W. 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