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Recommender Systems Using Collaborative Tagging

Recommender Systems Using Collaborative Tagging

Latha Banda, Karan Singh, Le Hoang Son, Mohamed Abdel-Basset, Pham Huy Thong, Hiep Xuan Huynh, David Taniar
Copyright: © 2020 |Volume: 16 |Issue: 3 |Pages: 18
ISSN: 1548-3924|EISSN: 1548-3932|EISBN13: 9781799804994|DOI: 10.4018/IJDWM.2020070110
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MLA

Banda, Latha, et al. "Recommender Systems Using Collaborative Tagging." IJDWM vol.16, no.3 2020: pp.183-200. http://doi.org/10.4018/IJDWM.2020070110

APA

Banda, L., Singh, K., Son, L. H., Abdel-Basset, M., Thong, P. H., Huynh, H. X., & Taniar, D. (2020). Recommender Systems Using Collaborative Tagging. International Journal of Data Warehousing and Mining (IJDWM), 16(3), 183-200. http://doi.org/10.4018/IJDWM.2020070110

Chicago

Banda, Latha, et al. "Recommender Systems Using Collaborative Tagging," International Journal of Data Warehousing and Mining (IJDWM) 16, no.3: 183-200. http://doi.org/10.4018/IJDWM.2020070110

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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%.

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