Exploiting latent social listening representations for music recommendations
Journal
CEUR Workshop Proceedings
Journal Volume
1441
Date Issued
2015-01-01
Author(s)
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 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.
Subjects
Factorization Machine | Graph | Recom-mender System | Representation Learning | Social Network
Type
conference paper