A collaborative filtering similarity measure based on potential field
Abstract
Purpose
Collaborative recommender systems play a crucial role in providing personalized services to online consumers. Most online shopping sites and many other applications now use the collaborative recommender systems. The measurement of the similarity plays a fundamental role in collaborative recommender systems. Some of the most well-known similarity measures are: Pearson’s correlation coefficient, cosine similarity and mean squared differences. However, due to data sparsity, accuracy of the above similarity measures decreases, which makes the formation of inaccurate neighborhood, thereby resulting in poor recommendations. The purpose of this paper is to propose a novel similarity measure based on potential field.
Design/methodology/approach
The proposed approach constructs a dense matrix: user-user potential matrix, and uses this matrix to compute potential similarities between users. Then the potential similarities are modified based on users’ preliminary neighborhoods, and k users with the highest modified similarity values are selected as the active user’s nearest neighbors. Compared to the rating matrix, the potential matrix is much denser. Thus, the sparsity problem can be efficiently alleviated. The similarity modification scheme considers the number of common neighbors of two users, which can further improve the accuracy of similarity computation.
Findings
Experimental results show that the proposed approach is superior to the traditional similarity measures.
Originality/value
The research highlights of this paper are as follows: the authors construct a dense matrix: user-user potential matrix, and use this matrix to compute potential similarities between users; the potential similarities are modified based on users’ preliminary neighborhoods, and k users with the highest modified similarity values are selected as the active user’s nearest neighbors; and the proposed approach performs better than the traditional similarity measures. The manuscript will be of particular interests to the scientists interested in recommender systems research as well as to readers interested in solution of related complex practical engineering problems.
Keywords
Acknowledgements
The authors would like to express the acknowledgements to the providers of MovieLens data set. This work was supported partially by the National Natural Science Foundation of China under the Grant Nos 713311002 and 51507099, partially by the Innovation Program of Shanghai Municipal Education Commission under the Grant No. 15ZS064, and partially by the Foundation for University Youth Teacher by the Shanghai Municipal Education Commission under the Grant No. ZZsdl15115.
Citation
Leng, Y., Lu, Q. and Liang, C. (2016), "A collaborative filtering similarity measure based on potential field", Kybernetes, Vol. 45 No. 3, pp. 434-445. https://doi.org/10.1108/K-10-2014-0212
Publisher
:Emerald Group Publishing Limited
Copyright © 2016, Emerald Group Publishing Limited