Collaborative Filtering Recommendation Algorithm Based on Improved Similarity Computing

DOI

10.2991/icmmcce-15.2015.262How to use a DOI?

Keywords

Personalized Recommendation; Collaborative Filtering; MAE (Mean Absolute Error); User Characteristic; Item Attribute

Abstract

At presently the most widely used algorithm is collaborative filtering in the Personalized Recommendation Systems for E-Commerce. Aiming at the problem that the recommendation is not accurate due to the data sparsity, a collaborative filtering algorithm based on user characteristics and item attributes preference was proposed in this study. It obtained the nearest neighbor users and similar items by analyzing the user characteristics, item attributes and the data of user's historical scores, and then computing the similarity between the two users based on the user-based collaborative filtering algorithm. It gave an algorithm which has the lower value of MAE and may improve the accuracy of the recommendation services.

Copyright

© 2015, 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  - Aili Liu
AU  - Baoan Li
PY  - 2015/12
DA  - 2015/12
TI  - Collaborative Filtering Recommendation Algorithm Based on Improved Similarity Computing
BT  - Proceedings of the 4th International Conference on Mechatronics, Materials, Chemistry and Computer Engineering 2015
PB  - Atlantis Press
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmmcce-15.2015.262
DO  - 10.2991/icmmcce-15.2015.262
ID  - Liu2015/12
ER  -