{"type":"ScholarlyArticle","title":"Explainable Recommendation via Interpretable Feature Mapping and Evaluation of Explainability","authors":[{"name":"Deng Pan","slug":"deng-pan","given":"Deng","family":"Pan","affiliation":"Department of Computer Science Wayne State University , USA"},{"name":"Xiangrui Li","slug":"xiangrui-li","given":"Xiangrui","family":"Li","affiliation":"Department of Computer Science Wayne State University , USA"},{"name":"Xin Li","slug":"xin-li","given":"Xin","family":"Li","affiliation":"Department of Computer Science Wayne State University , USA"},{"name":"Dongxiao Zhu","slug":"dongxiao-zhu","given":"Dongxiao","family":"Zhu","affiliation":"Department of Computer Science Wayne State University , USA"}],"abstract":"Latent factor collaborative filtering (CF) has been a widely used technique for recommender system by learning the semantic representations of users and items. Recently, explainable recommendation has attracted much attention from research community. However, trade-off exists between explainability and performance of the recommendation where metadata is often needed to alleviate the dilemma. We present a novel feature mapping approach that maps the uninterpretable general features onto the interpretable aspect features, achieving both satisfactory\r\naccuracy and explainability in the recommendations by simultaneous minimization of rating prediction loss and interpretation loss. To evaluate\r\nthe explainability, we propose two new evaluation metrics specifically designed for aspect-level explanation using surrogate ground truth. Experimental results demonstrate a strong performance in both recommendation and explaining explanation, eliminating the need for metadata. Code is available from https://github.com/pd90506/AMCF.","doi":"10.24963/ijcai.2020/373","url":"https://ijcai.org/proceedings/2020/373","pdf":"https://ijcai.org/proceedings/2020/0373.pdf","markdown":"https://ijcai.org/proceedings/2020/373.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/373","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":373,"pages":{"first":2690,"last":2696},"keywords":["Machine Learning: Recommender Systems","Machine Learning: Interpretability","Machine Learning: Explainable Machine Learning"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/25287/file_25287.m3u8","length_sec":289,"provider":"hls"}],"references":[{"position":1,"raw":"[Abdollahi and Nasraoui, 2016] Behnoush Abdollahi and Olfa Nasraoui. 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