Graph-Theoretic One-Class Collaborative Filtering using Signed Random Walk with Restart

Graph-theoretic one-class collaborative filtering (gOCCF) has been successful in dealing with sparse datasets in one-class setting (e.g., clicked or bookmarked). In this paper, we point out the problem that gOCCF requires long processing time compared to existing OCCF methods. To overcome the limitation of the original gOCCF, we propose a new gOCCF method based on signed random walk with restart (SRWR). Using SRWR, the proposed method accurately and efficiently captures users' preferences by analyzing not only positive preferences from rated items but also the negative preferences from uninteresting items. Through extensive experiments using real-life datasets, we verify that the proposed method improves the accuracy of the original gOCCF and requires processing time less than the original gOCCF.