{"type":"ScholarlyArticle","title":"Exploiting Music Play Sequence for Music Recommendation","authors":[{"name":"Zhiyong Cheng","slug":"zhiyong-cheng","given":"Zhiyong","family":"Cheng","affiliation":"School of Computing , National University of Singapore"},{"name":"Jialie Shen","slug":"jialie-shen","given":"Jialie","family":"Shen","affiliation":"Department of Computer and Information Sciences , Northumbria University , UK"},{"name":"Lei Zhu","slug":"lei-zhu","given":"Lei","family":"Zhu","affiliation":"School of Information Technology & Electrical Engineering , The University of Queensland"},{"name":"Mohan Kankanhalli","slug":"mohan-kankanhalli","given":"Mohan","family":"Kankanhalli","affiliation":"School of Computing , National University of Singapore"},{"name":"Liqiang Nie","slug":"liqiang-nie","given":"Liqiang","family":"Nie","affiliation":"School of Computer Science and Technology , Shandong University , China"}],"abstract":"Users leave digital footprints when interacting with various music streaming services. Music play sequence, which contains rich information about personal music preference and song similarity, has been largely ignored in previous music recommender systems. In this paper, we explore the effects of music play sequence on developing effective personalized music recommender systems. Towards the goal,  we propose to use word embedding techniques in music play sequences to estimate the similarity between songs. The learned similarity is then embedded into matrix factorization to boost the latent feature learning and discovery. Furthermore, the proposed method only considers the k-nearest songs (e.g., k = 5) in the learning process and thus avoids the increase of time complexity. Experimental results on two public datasets demonstrate that our methods could significantly improve the performance of both rating prediction and top-n recommendation tasks.","doi":"10.24963/ijcai.2017/511","url":"https://ijcai.org/proceedings/2017/511","pdf":"https://ijcai.org/proceedings/2017/0511.pdf","markdown":"https://ijcai.org/proceedings/2017/511.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/511","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Multidisciplinary Topics and Applications","track":"Main track","number":511,"pages":{"first":3654,"last":3660},"keywords":["Knowledge Representation, Reasoning, and Logic: Preference modelling and preference-based reasoning","Multidisciplinary Topics and Applications: Art and Music","Multidisciplinary Topics and Applications: Personalization and User Modeling"],"videos":[],"references":[{"position":1,"raw":"[Benzi et al., 2016] Kirell Benzi, Vassiiis Kalofolias, Xavier Bres- son, and Pierre Vandergheynst. 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