Measuring User Similarity Using Electric Circuit Analysis: Application to Collaborative Filtering

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We propose a new technique of measuring user similarity in collaborative filtering using electric circuit analysis. Electric circuit analysis is used to measure the potential differences between nodes on an electric circuit. In this paper, by applying this method to transaction networks comprising users and items, i.e., user-item matrix, and by using the full information about the relationship structure of users in the perspective of item adoption, we overcome the limitations of one-to-one similarity calculation approach, such as the Pearson correlation, Tanimoto coefficient, and Hamming distance, in collaborative filtering. We found that electric circuit analysis can be successfully incorporated into recommender systems and has the potential to significantly enhance predictability, especially when combined with user-based collaborative filtering. We also propose four types of hybrid algorithms that combine the Pearson correlation method and electric circuit analysis. One of the algorithms exceeds the performance of the traditional collaborative filtering by 37.5% at most. This work opens new opportunities for interdisciplinary research between physics and computer science and the development of new recommendation systems
Publisher
PUBLIC LIBRARY SCIENCE
Issue Date
2012-11
Language
English
Article Type
Article
Keywords

RECOMMENDER SYSTEMS; SOCIAL NETWORKS; INFORMATION; GRAPHS

Citation

PLOS ONE, v.7, no.11, pp.01 - 10

ISSN
1932-6203
DOI
10.1371/journal.pone.0049126
URI
http://hdl.handle.net/10203/223393
Appears in Collection
MG-Journal Papers(저널논문)
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