{"type":"ScholarlyArticle","title":"Argumentation-Based Recommendations: Fantastic Explanations and How to Find Them","authors":[{"name":"Antonio Rago","slug":"antonio-rago","given":"Antonio","family":"Rago","affiliation":"Department of Computing , Imperial College London , UK"},{"name":"Oana Cocarascu","slug":"oana-cocarascu","given":"Oana","family":"Cocarascu","affiliation":"Department of Computing , Imperial College London , UK"},{"name":"Francesca Toni","slug":"francesca-toni","given":"Francesca","family":"Toni","affiliation":"Department of Computing , Imperial College London , UK"}],"abstract":"A significant problem of recommender systems is their inability to explain recommendations, resulting in turn in ineffective feedback from users and the inability to adapt to users’ preferences. We propose a hybrid method for calculating predicted ratings, built upon an item/aspect-based graph with users’ partially given ratings, that can be naturally used to provide explanations for recommendations, extracted from user-tailored Tripolar Argumentation Frameworks (TFs). We show that our method can be understood as a gradual semantics for TFs, exhibiting a desirable, albeit weak, property of balance. We also show experimentally that our method is competitive in generating correct predictions, compared with state-of-the-art methods, and illustrate how users can interact with the generated explanations to improve quality of recommendations.","doi":"10.24963/ijcai.2018/269","url":"https://ijcai.org/proceedings/2018/269","pdf":"https://ijcai.org/proceedings/2018/0269.pdf","markdown":"https://ijcai.org/proceedings/2018/269.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/269","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Knowledge Representation and Reasoning","track":"Main track","number":269,"pages":{"first":1949,"last":1955},"keywords":["Knowledge Representation and Reasoning: Computational Models of Argument","Machine Learning: Recommender Systems"],"videos":[],"references":[{"position":1,"raw":"[Aggarwal, 2016] Charu C. 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