Chen, Chih MingChih MingChenChien, Po ChuanPo ChuanChienLin, Yu ChingYu ChingLinTsai, Ming FengMing FengTsaiYI-HSUAN YANG2023-10-202023-10-202015-01-0116130073https://scholars.lib.ntu.edu.tw/handle/123456789/636369Music 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 difficult, 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 relationships can be transformed from the sparse and independent and identically distributed (i.i.d.) form into a dense and noni.i.d. form. In addition, the latent representations can also capture the spatial locality among users and items, therefore benefiting the constructed recommendation models.Factorization Machine | Graph | Recom-mender System | Representation Learning | Social Networkconference paper2-s2.0-84944706566https://api.elsevier.com/content/abstract/scopus_id/84944706566