One Improved Collaborative Filtering Method Based on Information Transformation

Zhaoxing Liu, Ning Zhang

Abstract


In this paper, we propose a novel method combined classical collaborative filtering (CF) and bipartite network structure. Different from the classical CF, user similarity is viewed as personal recommendation power and during the recommendation process; it will be redistributed to different users. Furthermore, a free parameter is introduced to tune the contribution of the user to the user similarity. Numerical results demonstrate that decreasing the degree of user to some extent in method performs well in rank value and hamming distance. Furthermore, the correlation between degree and similarity is concerned to solved the drastically change of our method performance.


Full Text: PDF DOI: 10.5539/cis.v4n1p186

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.

Computer and Information Science   ISSN 1913-8989 (Print)   ISSN 1913-8997 (Online)
Copyright © Canadian Center of Science and Education

To make sure that you can receive messages from us, please add the 'ccsenet.org' domain to your e-mail 'safe list'. If you do not receive e-mail in your 'inbox', check your 'bulk mail' or 'junk mail' folders.