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Time Cluster Personalized Ranking Recommender System in Multi-Cloud

Author

Listed:
  • S. Abinaya

    (School of Computer Science and Engineering, Vellore Institute of Technology, Chennai 600127, India)

  • K. Indira

    (Department of Information Technology, Thiagarajar College of Engineering, Madurai 625015, India)

  • S. Karthiga

    (Department of Information Technology, Thiagarajar College of Engineering, Madurai 625015, India)

  • T. Rajasenbagam

    (Department of Computer Science and Engineering, Government College of Technology, Coimbatore 641013, India)

Abstract

Recommender systems have become a vital tool to identify items for users based on personalized preferences. The personalized ranking or item recommendation generates a ranked list of items for the users. Clustering methods offer better scalability than collaborative filtering (CF) methods since they make predictions within small clusters. The major challenges of recommender systems are accuracy and scalability. Traditionally, recommendation systems are based on a centralized framework that restrains quick scalability for enormous data volumes. The emergence of cloud technology resolves this issue as it handles vast data and supports massive processing. This paper proposes a time cluster personalized ranking recommender system (TCPRRS) in a multi-cloud environment. TCPRRS is a five-stage system that generates recommendations based on temporal information of user consumption and clustering with personalized ranking. Particle swarm optimization (PSO) is utilized for optimizing the solution. The efficiency of TCPRRS is estimated using similarity metrics.

Suggested Citation

  • S. Abinaya & K. Indira & S. Karthiga & T. Rajasenbagam, 2023. "Time Cluster Personalized Ranking Recommender System in Multi-Cloud," Mathematics, MDPI, vol. 11(6), pages 1-17, March.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:6:p:1300-:d:1091162
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    References listed on IDEAS

    as
    1. Asim Ansari & Yang Li & Jonathan Z. Zhang, 2018. "Probabilistic Topic Model for Hybrid Recommender Systems: A Stochastic Variational Bayesian Approach," Marketing Science, INFORMS, vol. 37(6), pages 987-1008, November.
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