A Collaborative Filtering Recommendation Algorithm with Improved Similarity Calculation

DOI

10.2991/icsnce-18.2018.32How to use a DOI?

Keywords

Recommendation Algorithm; Collaborative Filtering; Similarity Calculation; Baseline Predictors Model

Abstract

In order to improve the accuracy of the proposed algorithm in collaborative filtering recommendation system, an Improved Pearson collaborative filtering (IP-CF) algorithm is proposed in this paper. The algorithm uses the user portrait, item characteristics and data of user behavior to compute the baseline predictors model. Instead of the traditional algorithm's similarity calculation, the prediction model is used to improve the accuracy of the recommendation algorithm. Experimental results on Moivelens dataset show that the IP-CF algorithm significantly improves the accuracy of the recommended results, and the RMSE and MAE evaluation results are better than the traditional algorithms.

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/).

Download article (PDF)

TY  - CONF
AU  - Yang Ju
AU  - Liu Bailin
AU  - Zhao Zhixiang
PY  - 2018/04
DA  - 2018/04
TI  - A Collaborative Filtering Recommendation Algorithm with Improved Similarity Calculation
BT  - Proceedings of the 2018 Second International Conference of Sensor Network and Computer Engineering (ICSNCE 2018)
PB  - Atlantis Press
SP  - 158
EP  - 161
SN  - 2352-538X
UR  - https://doi.org/10.2991/icsnce-18.2018.32
DO  - 10.2991/icsnce-18.2018.32
ID  - Ju2018/04
ER  -