{"type":"ScholarlyArticle","title":"Interpretable Recommendation via Attraction Modeling: Learning Multilevel Attractiveness over Multimodal Movie Contents","authors":[{"name":"Liang Hu","slug":"liang-hu","given":"Liang","family":"Hu","affiliation":"Advanced Analytics Institute , University of Technology , Sydney; Institute of Network Computing & IoT , University of Shanghai for Science and Technology"},{"name":"Songlei Jian","slug":"songlei-jian","given":"Songlei","family":"Jian","affiliation":"Advanced Analytics Institute , University of Technology , Sydney; College of Computer , National University of Defense Technology , China"},{"name":"Longbing Cao","slug":"longbing-cao","given":"Longbing","family":"Cao","affiliation":"Advanced Analytics Institute , University of Technology , Sydney"},{"name":"Qingkui Chen","slug":"qingkui-chen","given":"Qingkui","family":"Chen","affiliation":"Institute of Network Computing & IoT , University of Shanghai for Science and Technology"}],"abstract":"New contents like blogs and online videos are produced in every second in the new media age. We argue that attraction is one of the decisive factors for user selection of new contents. However, collaborative filtering cannot work without user feedback; and the existing content-based recommender systems are ineligible to capture and interpret the attractive points on new contents. Accordingly, we propose attraction modeling to learn and interpret user attractiveness. Specially, we build a multilevel attraction model (MLAM) over the content features -- the story (textual data) and cast members (categorical data) of movies. In particular, we design multilevel personal filters to calculate users' attractiveness on words, sentences and cast members at different levels. The experimental results show the superiority of MLAM over the state-of-the-art methods. In addition, a case study is provided to demonstrate the interpretability of MLAM by visualizing user attractiveness on a movie.","doi":"10.24963/ijcai.2018/472","url":"https://ijcai.org/proceedings/2018/472","pdf":"https://ijcai.org/proceedings/2018/0472.pdf","markdown":"https://ijcai.org/proceedings/2018/472.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/472","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":"Machine Learning Applications","track":"Main track","number":472,"pages":{"first":3400,"last":3406},"keywords":["Machine Learning: Recommender Systems","Natural Language Processing: Embeddings","Machine Learning: Learning Preferences or Rankings","Machine Learning: Neural Networks","Machine Learning: Interpretability"],"videos":[],"references":[{"position":1,"raw":"[Agarwal and Chen, 2010] Deepak Agarwal and Bee-Chung Chen. flda: matrix factorization through latent dirichlet al- location. 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