Collaborative Filtering Algorithm Based on Item Attribute and Time Weight

DOI

10.2991/icacie-16.2016.3How to use a DOI?

Keywords

Collaborative filtering, Sparse data, Item attribute, Time weight

Abstract

Collaborative filtering is a recommendation algorithm which is used in personalized system. To solve the problem of low accuracy caused by sparse data in the user-item matrix of traditional collaborative filtering algorithm, this paper presents a hybrid algorithm. Firstly, it uses the data based on the similarity of item's attributes to fill the matrix. And then a weight decrease by the time is given to increase the effectiveness of the measurement, thereby to improve the accuracy of the collaborative filtering algorithm. Experimental results show that the algorithm proposed in this paper can improve the accuracy of recognition and enhance the quality of the recommendation system.

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/).

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TY  - CONF
AU  - Qian Chen
AU  - Wanggen Li
AU  - Jiao Liu
PY  - 2016/10
DA  - 2016/10
TI  - Collaborative Filtering Algorithm Based on Item Attribute and Time Weight
BT  - Proceedings of the 2016 International Conference on Automatic Control and Information Engineering
PB  - Atlantis Press
SP  - 12
EP  - 15
SN  - 2352-5401
UR  - https://doi.org/10.2991/icacie-16.2016.3
DO  - 10.2991/icacie-16.2016.3
ID  - Chen2016/10
ER  -