{"type":"ScholarlyArticle","title":"Co-Attentive Multi-Task Learning for Explainable 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 , China; Microsoft Research Asia , China"},{"name":"Xiting Wang","slug":"xiting-wang","given":"Xiting","family":"Wang","affiliation":"Microsoft Research Asia , China"},{"name":"Xing Xie","slug":"xing-xie","given":"Xing","family":"Xie","affiliation":"Microsoft Research Asia , China"},{"name":"Tong Wu","slug":"tong-wu","given":"Tong","family":"Wu","affiliation":"CFETS Information Technology (Shanghai) Co. , Ltd. , China"},{"name":"Guoqing Bu","slug":"guoqing-bu","given":"Guoqing","family":"Bu","affiliation":"CFETS Information Technology (Shanghai) Co. , Ltd. , China"},{"name":"Yining Wang","slug":"yining-wang","given":"Yining","family":"Wang","affiliation":"CFETS Information Technology (Shanghai) Co. , Ltd. , China"},{"name":"Enhong Chen","slug":"enhong-chen","given":"Enhong","family":"Chen","affiliation":"School of Computer Science and Technology , University of Science and Technology of China , China"}],"abstract":"Despite widespread adoption, recommender systems remain mostly black boxes. Recently, providing explanations about why items are recommended has attracted increasing attention due to its capability to enhance user trust and satisfaction. In this paper, we propose a co-attentive multi-task learning model for explainable recommendation. Our model improves both prediction accuracy and explainability of recommendation by fully exploiting the correlations between the recommendation task and the explanation task. In particular, we design an encoder-selector-decoder architecture inspired by human's information-processing model in cognitive psychology. We also propose a hierarchical co-attentive selector to effectively model the cross knowledge transferred for both tasks. Our model not only enhances prediction accuracy of the recommendation task, but also generates linguistic explanations that are fluent, useful, and highly personalized. Experiments on three public datasets demonstrate the effectiveness of our model.","doi":"10.24963/ijcai.2019/296","url":"https://ijcai.org/proceedings/2019/296","pdf":"https://ijcai.org/proceedings/2019/0296.pdf","markdown":"https://ijcai.org/proceedings/2019/296.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/296","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Machine Learning A-L","track":"Main track","number":296,"pages":{"first":2137,"last":2143},"keywords":["Machine Learning: Explainable Machine Learning","Natural Language Processing: Natural Language Generation","Machine Learning: Recommender Systems"],"videos":[],"references":[{"position":1,"raw":"[Alvarez-Melis and Jaakkola, 2018] David Alvarez-Melis and Tommi S. 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