Proceedings of the 2016 International Conference on Automatic Control and Information Engineering

Collaborative Filtering Algorithm Based on Item Attribute and Time Weight

Authors
Qian Chen, Wanggen Li, Jiao Liu
Corresponding Author
Qian Chen
Available Online October 2016.
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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Volume Title
Proceedings of the 2016 International Conference on Automatic Control and Information Engineering
Series
Advances in Engineering Research
Publication Date
October 2016
ISBN
978-94-6252-254-1
ISSN
2352-5401
DOI
10.2991/icacie-16.2016.3How to use a DOI?
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/).

Cite this article

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  -