A collaborative filtering similarity measure based on potential field. Issue 3 (7th March 2016)

• Record Type: Journal Article
• Authors: Leng, Yajun; Lu, Qing; Liang, Changyong
• Is Part Of: Kybernetes. Volume 45:Issue 3(2016)
• Page Start: 434
• Page End: 445
• Publication Date: 2016-03-07
• Subjects: Algorithms -- Systems theory; Cybernetics -- Periodicals; Systems engineering -- Periodicals; 003.505
• DOI: 10.1108/K-10-2014-0212
• Languages: English
• ISSNs: 0368-492X

Abstract

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.