Design and Implementation of Music Recommendation System Based on Hadoop

DOI

10.2991/icsnce-18.2018.36How to use a DOI?

Keywords

Music Recommendation; K-means Clustering; Collaborative Filtering; Recommendation Algorithm; Hadoop

Abstract

In order to solve the problem of information overload of music system under large data background, this paper studies the design scheme of distributed music recommendation system based on Hadoop. The proposed algorithm is based on the MapReduce distributed computing framework, which has high scalability and performance, and can be applied to the calculation and analysis of off-line data efficiently. The music recommendation system designed in this paper also includes client, server interface, database and ETL operation, which can calculate a set of complete recommendation system from user operation end to server and data calculation. In order to improve the accuracy of the recommendation algorithm, this paper introduces k-means clustering algorithm to improve the recommendation algorithm based on user-based collaborative filtering. The experimental results show that the accuracy of the proposed algorithm has been significantly improved after the introduction of k-means.

Copyright

© 2018, 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  - Zhao Yufeng
AU  - Li Xinwei
PY  - 2018/04
DA  - 2018/04
TI  - Design and Implementation of Music Recommendation System Based on Hadoop
BT  - Proceedings of the 2018 Second International Conference of Sensor Network and Computer Engineering (ICSNCE 2018)
PB  - Atlantis Press
SP  - 183
EP  - 189
SN  - 2352-538X
UR  - https://doi.org/10.2991/icsnce-18.2018.36
DO  - 10.2991/icsnce-18.2018.36
ID  - Yufeng2018/04
ER  -