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A trust-worthy approach to recommend movies for communities

  • 1205: Emerging Technologies for Information Hiding and Forensics in Multimedia Systems
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Abstract

Trustworthy recommendation of a movie is a highly complex task for the entertainment industry wherein trust is s a crucial metric of recommendation systems. It depends upon various factors, such as preferences, reviews, emotions, promotions and sentiments. However, these factors are specific to individuals and may vary from person to person. Additionally, the data collected for movie recommendations suffer from data sparsity and cold start problems. Previous studies on movie recommendations have failed to be trustworthy because their performance is greatly affected by fake ratings, data sparsity, and cold start problems. Also, the existing models of Recommender Systems (RS) do not consider the trust score and the user’s rating criterion. Keeping this in view, in this paper, the rating and ranking criteria with a trust score of different users is incorporated into the proposed machine learning-based RS models to ensure the trustworthiness of the system. In particular, one can notice that the most relevant viewers have the same taste and preferences. So, the bipartite relationship between the movie and the viewer has been interpreted through the inversion similarity concept which is used to design an efficient and trustworthy movie recommendation model for a community of viewers. The proposed model uses a learning algorithm to measure the trust score of recommendations and also performs cluster analysis to identify the groups having similar behavior in their communities. The information extracted from the cluster analysis identifies the user’s pattern of movie watching and predicts their movie selection behavior. We have performed extensive experiments to find and compare the performance of the proposed models with other existing models. The results of the experiments demonstrated the better performance of proposed models and supported the claim.

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Authors and Affiliations

  1. Bhagalpur College of Engineering, Bhagalpur, India

    Govind Kumar Jha

  2. Institute of Engineering and Technology, Dr. APJ Abdul Kalam Technical University Lucknow, Lucknow, India

    Manish Gaur

  3. Manav Rachna University Faridabad, Faridabad, India

    Hardeo Kumar Thakur

Authors
  1. Govind Kumar Jha
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  2. Manish Gaur
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  3. Hardeo Kumar Thakur
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Jha, G.K., Gaur, M. & Thakur, H.K. A trust-worthy approach to recommend movies for communities. Multimed Tools Appl 81, 19655–19682 (2022). https://doi.org/10.1007/s11042-021-11544-1

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  • DOI: https://doi.org/10.1007/s11042-021-11544-1

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