Exploiting Latent Social Listening Representations for Music Recommendations

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Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/98248

Title:	Exploiting Latent Social Listening Representations for Music Recommendations
Authors:	Chen, Chih-Ming;Chien, Po-Chuan;Lin, Yu-Ching;Tsai, Ming-Feng;Yang, Yi-Hsuan 陳志明;蔡銘峰
Contributors:	資科系
Keywords:	Representation Learning, Factorization Machine, Recommender System, Social Network, Graph
Date:	2015-09
Issue Date:	2016-06-22 17:20:04 (UTC+8)
Abstract:	Music listening can be regarded as a social activity, in which people can listen together and make friends with one other. Therefore, social relationships may imply multiple facets of the users, such as their listening behaviors and tastes. In this light, it is considered that social relationships hold abundant valuable information that can be utilized for music recommendation. However, utilizing the information for recommendation could be di cult, because such information is usually sparse. To address this issue, we propose to learn the latent social listening representations by the DeepWalk method, and then integrate the learned representations into Factorization Machines to construct better recommendation models. With the DeepWalk method, user social relation-ships can be transformed from the sparse and independent and identically distributed (i.i.d.) form into a dense and non-i.i.d. form. In addition, the latent representations can also capture the spatial locality among users and items, therefore bene ting the constructed recommendation models.
Relation:	Poster Proceedings of the 9th ACM Conference on Recommender Systems (RecSys '15), 2015
Data Type:	conference
Appears in Collections:	[資訊科學系] 會議論文

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