Research on User-based Normalization Collaborative Filtering Recommendation Algorithm

DOI

10.2991/ameii-16.2016.294How to use a DOI?

Keywords

Normalization, Collaborative Filtering, Sparsity, Recommendation Algorithm

Abstract

Under the circumstance of the big data, because of the low efficiency and low performance of analysis and calculation in stand-alone mode, the traditional recommendation algorithm is limited greatly, recommended time and recommended precision is difficult to guarantee. This thesis makes a improvement on the user-based collaborative filtering algorithm. The user similarity calculation is based on the normalized method, which makes user rating more reasonable and reduces the data sparse. Meanwhile the algorithm can be run on the massive clusters, which improve the operation efficiency of the system and the scalability of the system significantly. Finally, designing the scheme of recommendation system experiments and the experimental results show that the accuracy of the improved algorithm is superior to the traditional collaborative filtering algorithm and strengthen the efficiency at the same time.

Copyright

© 2016, 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/).

Download article (PDF)

TY  - CONF
AU  - Jie Dong
AU  - Jin Li
AU  - Gui Li
AU  - Liming Du
PY  - 2016/04
DA  - 2016/04
TI  - Research on User-based Normalization Collaborative Filtering Recommendation Algorithm
BT  - Proceedings of the 2nd International Conference on Advances in Mechanical Engineering and Industrial Informatics (AMEII 2016)
PB  - Atlantis Press
SN  - 2352-5401
UR  - https://doi.org/10.2991/ameii-16.2016.294
DO  - 10.2991/ameii-16.2016.294
ID  - Dong2016/04
ER  -