title: Measuring user similarity using electric circuit analysis: application to collaborative filtering.
abstract: 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.
authors.affiliation: Graduate School of Culture Technology, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea.
authors.forename: Joonhyuk
authors.initials: J
authors.lastname: Yang
authors.forename: Jinwook
authors.initials: J
authors.lastname: Kim
authors.forename: Wonjoon
authors.initials: W
authors.lastname: Kim
authors.forename: Young Hwan
authors.initials: YH
authors.lastname: Kim
mesh_terms: D000465:Algorithms; D003299:Cooperative Behavior; D006801:Humans; D060756:Social Networking; D012984:Software