标题:Learning music embedding with metadata for context aware recommendation
作者:Wang, Dongjing ;Deng, Shuiguang ;Zhang, Xin ;Xu, Guandong
作者机构:[Wang, Dongjing ;Deng, Shuiguang ] College of Computer Science and Technology, Zhejiang University, Hangzhou, Zhejiang, China;[Wang, Dongjing ;Xu, Gua 更多
会议名称:6th ACM International Conference on Multimedia Retrieval, ICMR 2016
会议日期:6 June 2016 through 9 June 2016
来源:ICMR 2016 - Proceedings of the 2016 ACM International Conference on Multimedia Retrieval
出版年:2016
页码:249-253
DOI:10.1145/2911996.2912045
关键词:Context aware recommendation; Embedding; Music recommendation; Recommender systems
摘要:Contextual factors can benefit music recommendation and retrieval tasks remarkably. However, how to acquire and utilize the contextual information still need to be studied. In this paper, we propose a context aware music recommendation approach, which can recommend music appropriate for users' contextual preference for music. In analogy to matrix factorization methods for collaborative filtering, the proposed approach does not require songs to be described by features beforehand, but it learns music pieces' embeddings (vectors in low-dimensional continuous space) from music playing records and corresponding metadata and infer users' general and contextual preference for music from their playing records with the learned embedding. Then, our approach can recommend appropriate music pieces. Experimental evaluations on a real world dataset show that the proposed approach outperforms baseline methods. © 2016 ACM.
收录类别:EI;SCOPUS
Scopus被引频次:4
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-84978734456&doi=10.1145%2f2911996.2912045&partnerID=40&md5=b2930cb71e56378f271c321c2e2f534a
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