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Multimedia Tools and Applications

, Volume 78, Issue 3, pp 2667–2687 | Cite as

Improving music recommendation by incorporating social influence

  • Jinpeng ChenEmail author
  • Pinguang Ying
  • Ming Zou
Article
  • 499 Downloads

Abstract

In the past decades, a large number of music pieces are uploaded to the Internet every day through social networks, such as Last.fm, Spotify and YouTube, that concentrates on music and videos. We have been witnessing an ever-increasing amount of music data. At the same time, with the huge amount of online music data, users are facing an everyday struggle to obtain their interested music pieces. To solve this problem, music search and recommendation systems are helpful for users to find their favorite content from a huge repository of music. However, social influence, which contains rich information about similar interests between users and users’ frequent correlation actions, has been largely ignored in previous music recommender systems. In this work, we explore the effects of social influence on developing effective music recommender systems and focus on the problem of social influence aware music recommendation, which aims at recommending a list of music tracks for a target user. To exploit social influence in social influence aware music recommendation, we first construct a heterogeneous social network, propose a novel meta path-based similarity measure called WPC, and denote the framework of similarity measure in this network. As a step further, we use the topological potential approach to mine social influence in heterogeneous networks. Finally, in order to improve music recommendation by incorporating social influence, we present a factor graphic model based on social influence. Our experimental results on one real world dataset verify that our proposed approach outperforms current state-of-the-art music recommendation methods substantially.

Keywords

Music recommendation Topological potential Social influence Meta-Path 

Notes

Acknowledgments

This work is supported by the National Natural Science Foundation of China under Grant No.61702043, and the Fundamental Research Funds for the Central Universities under Grant No. 500417062.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Software EngineeringBeijing University of Posts and TelecommunicationsBeijingChina
  2. 2.School of WTO Research and EducationShanghai University of International Business and EconomicsShanghaiChina
  3. 3.State Key Laboratory of Software Development EnvironmentBeihang UniversityBeijingChina

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