A Personalized Music Recommendation Algorithm Based on User Implicit Feedback and Weighted Socialized Tag Content Filtering

DOI

10.2991/icemaess-17.2017.69How to use a DOI?

Keywords

Personalized Recommendation, Implicit Feedback, Socialized Tag, Content Filtering

Abstract

The popularity of the Internet and the electronization of music resources make it easier for people to get music what they like. However, facing abundant resources, it is difficult for people to find their favorite music accurately and timely. Personalized music recommendation algorithms play an increasingly important role in online music service systems. Traditional feedback method requires the user to give feedback explicitly, which not only increases information collection cost but also gives the user an additional burden. This paper proposes a content filtering recommendation method based on user implicit feedback and weighted socialized tags,and designs experiment to compare our recommendation method with fuzzy theory method. The experimental results show this method is more accurate than the fuzzy theory method in recommending music to the user.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Yang Yang
AU  - Qiang Wang
PY  - 2017/12
DA  - 2017/12
TI  - A Personalized Music Recommendation Algorithm Based on User Implicit Feedback and Weighted Socialized Tag Content Filtering
BT  - Proceedings of the 4th International Conference on Education, Management, Arts, Economics and Social Science (ICEMAESS 2017)
PB  - Atlantis Press
SP  - 309
EP  - 313
SN  - 2352-5398
UR  - https://doi.org/10.2991/icemaess-17.2017.69
DO  - 10.2991/icemaess-17.2017.69
ID  - Yang2017/12
ER  -