Bias-corrected-based collaborative filtering recommendation (Bias-Corr-CF)

• Published in: PloS one Vol. 20; no. 6; p. e0324173
• Main Authors: Tran, Tu Cam Thi, Huynh, Hiep Xuan
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 30.06.2025; Public Library of Science (PLoS)
• Subjects: Algorithms; Analysis; Bias; Biology and Life Sciences; Collaboration; Computer and Information Sciences; Correlation; Datasets; Energy; Engineering and Technology; Filters (Mathematics); Filtration; Humans; Information storage and retrieval; Mathematical models; Methods; Models, Theoretical; Motion pictures; Physical Sciences; Random variables; Ratings & rankings; Recall; Recommender systems; Research and Analysis Methods; Set (Psychology); Social Sciences; Statistical analysis; Statistics; Vietnam
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0324173

The goal of the collaborative filtering problem is to find accurate and efficient mappings from previously rated data at items of the users. Improving item-based collaborative filtering (IBCF) and user-based collaborative filtering (UBCF) involves understanding the mathematics of distance measures and finding the right balance between calculating similarity and providing recommendations very accurate. However, the popular distance measures for recommendation models only focus on measuring pairwise rating values between one user and another, or between one item and another. In this article, the authors have proposed a new recommendation model, which consists of building a collaborative filtering model with the bias-corrected distance correlation statistic. The correlation method focuses on measuring the rating values of one object with all ratings of the other object; the Bias-Corrected Distance Correlation (BCDCOR) provides an improved estimate of the distance correlation; it corrects the bias present in the original distance correlation. Experimental results are developed on the Jester5k dataset, with two popular evaluation methods for the recommendation models, namely precision and recall values. The experimental results show that with the Bias-corrected-based recommendation model between users and users, the Precision and Recall values of the proposed model are higher than those of the compared collaborative filtering recommendation systems.