Spark-based Parallel Collaborative Filtering Recommendation Algorithm

DOI

10.2991/iccia-17.2017.179How to use a DOI?

Keywords

Collaborative Filtering Recommendation Algorithm, RLPSO, K-means, Spark.

Abstract

The rapid development of Internet information technology makes the problem of information overload become more and more serious, and recommendation system is one of the effective ways to solve this problem which is favored by people. However, for the massive data information, the recommended algorithm faces the bottleneck problem of processing speed and computing resources, so this paper proposed a parallel collaborative filtering recommendation algorithm based on Spark. The RLPSO algorithm is used to optimize the clustering factor of the K-means clustering algorithm by associating users with similar interests into a cluster and using the recommended algorithm for users to recommend is implemented on the Spark platform. The experimental results show that the improved algorithm has a significant improvement in the prediction accuracy, and has a higher speedup and stability compared with the traditional collaborative filtering recommendation algorithm.

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  - Yongli Yang
AU  - Fei Xue
AU  - Yongquan Cai
AU  - Zhenhu Ning
PY  - 2016/07
DA  - 2016/07
TI  - Spark-based Parallel Collaborative Filtering Recommendation Algorithm
BT  - Proceedings of the 2nd International Conference on Computer Engineering, Information Science & Application Technology (ICCIA 2017)
PB  - Atlantis Press
SP  - 1014
EP  - 1017
SN  - 2352-538X
UR  - https://doi.org/10.2991/iccia-17.2017.179
DO  - 10.2991/iccia-17.2017.179
ID  - Yang2016/07
ER  -