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A proficient video recommendation framework using hybrid fuzzy C means clustering and Kullback-Leibler divergence algorithms

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Abstract

A video recommendation framework for e-commerce clients is proposed using the collaborative filtering (CF) process. One of the most important features of the CF algorithm is its scalability. To avoid the issue, a hybrid model-based collaborative filtering approach is proposed. KL Divergence was developed to address the CF technique’s scalability problem. The clustering with enhanced sqrt-cosine similarity Recommender scheme is proposed. For successful clustering, Kullback–Leibler Divergence-based Fuzzy C-Means clustering is suggested, with the aim of focusing on greater accuracy during movie recommendation.The proposed scheme is viewed as a trustworthy contribution that significantly improves the ability of movie recommendation by virtue of the KL divergence-based Fuzzy C-Means clustering mechanism and enhanced sqrt-cosine similarity. The proposed scheme highlighted and addressed the critical role of the KL divergence-based cluster ensemble factor in improving clustering stability and robustness. For prediction, the enhanced sqrt-cosine similarity was used to calculate successful related neighbor users. The performance of Recommendation is improved when KLD-FCM is combined with improved sqrt-cosine similarity.The proposed scheme’s empirical work on the Movielens dataset in terms of MAE, RMSE, SD, and Recall were found to be superior in recommendation accuracy compared to traditional approaches and some non-clustering based methods recommended for study. With the specified number of clusters, it is capable of providing accurate and customized movie recommendation systems.

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Authors and Affiliations

  1. School of Computer Science and Engineering, REVA University, Bengaluru, India

    H. Anwar Basha

  2. Department of Computer Science and Engineering, College of Engineering and Technology, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Vadapalani Campus, Chennai, India

    S. K. B Sangeetha & J. Arunnehru

  3. Department of Computer Science and Engineering, Saveetha Engineering College, Chennai, India

    S. Sasikumar

  4. Department of CSE , Chaitanya Bharathi Institute of Technology, Hyderabad, India

    M. Subramaniam

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  1. H. Anwar Basha
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  2. S. K. B Sangeetha
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  3. S. Sasikumar
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  4. J. Arunnehru
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  5. M. Subramaniam
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Correspondence to H. Anwar Basha.

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Basha, H.A., Sangeetha, S.K.B., Sasikumar, S. et al. A proficient video recommendation framework using hybrid fuzzy C means clustering and Kullback-Leibler divergence algorithms. Multimed Tools Appl 82, 20989–21004 (2023). https://doi.org/10.1007/s11042-023-14460-8

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  • DOI: https://doi.org/10.1007/s11042-023-14460-8

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