Collaborative Filtering Algorithm Based on the Similarity of User Ratings and Item Attributes

DOI

10.2991/icmii-15.2015.78How to use a DOI?

Keywords

Collaborative Filtering; Personalized Recommendation; Data Sparsity; Item Attributes; Similarity

Abstract

Collaborative filtering recommendation algorithm is key technologies of personalized recommendation system, as the serious data sparsity of rated items, the traditional collaborative filtering algorithms only depending on users data cannot achieve satisfactory recommended quality, an improved collaborative filtering recommendation algorithm based on the similarity of user ratings and item attributes is proposed. The experimental results based on Movie Lens dataset show that the improved hybrid collaborative filtering recommendation algorithm obtains the better recommendation accuracy than traditional similarity calculation method.

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  - 2015/10
DA  - 2015/10
TI  - Collaborative Filtering Algorithm Based on the Similarity of User Ratings and Item Attributes
BT  - Proceedings of the 3rd International Conference on Mechatronics and Industrial Informatics
PB  - Atlantis Press
SP  - 451
EP  - 455
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmii-15.2015.78
DO  - 10.2991/icmii-15.2015.78
ID  - Liu2015/10
ER  -