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] Zhejiang Univ, Coll Comp Sci & Technol, Hangzhou, Zhejiang, Peoples R China.; [Zhang, Xin] Shandong Univ, Sch Comp 更多
会议名称：ACM International Conference on Multimedia Retrieval (ICMR)
会议日期：JUN 06-09, 2016
来源：ICMR'16: PROCEEDINGS OF THE 2016 ACM INTERNATIONAL CONFERENCE ON MULTIMEDIA RETRIEVAL
关键词：Recommender Systems; Music Recommendation; Context Aware Recommendation;; Embedding
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.