Recommender Systems Using Collaborative Tagging

Latha Banda, Karan Singh, Le Hoang Son, Mohamed Abdel-Basset, Pham Huy Thong, Hiep Xuan Huynh and David Taniar
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Latha Banda: School of Computer and System Science, Sharda University, India
Karan Singh: School of Computer and System Science, Jawaharlal Nehru University, India
Le Hoang Son: Institute of Research and Development, Duy Tan University, Da Nang, Vietnam & VNU Information Technology Institute, Vietnam National University, Hanoi, Vietnam
Mohamed Abdel-Basset: Department of Operations Research, Faculty of Computers and Informatics, Zagazig University, Egypt
Pham Huy Thong: Ton Duc Thang University, Vietnam
Hiep Xuan Huynh: College of Information and Communication Technology, Can Tho University, Vietnam
David Taniar: Faculty of Information Technology, Monash University, Australia

International Journal of Data Warehousing and Mining (IJDWM), 2020, vol. 16, issue 3, 183-200

Abstract: Collaborative tagging is a useful and effective way for classifying items with respect to search, sharing information so that users can be tagged via online social networking. This article proposes a novel recommender system for collaborative tagging in which the genre interestingness measure and gradual decay are utilized with diffusion similarity. The comparison has been done on the benchmark recommender system datasets namely MovieLens, Amazon datasets against the existing approaches such as collaborative filtering based on tagging using E-FCM, and E-GK clustering algorithms, hybrid recommender systems based on tagging using GA and collaborative tagging using incremental clustering with trust. The experimental results ensure that the proposed approach achieves maximum prediction accuracy ratio of 9.25% for average of various splits data of 100 users, which is higher than the existing approaches obtained only prediction accuracy of 5.76%.

Date: 2020
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