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A hybrid group-based movie recommendation framework with overlapping memberships

Author

Listed:
  • Yasher Ali
  • Osman Khalid
  • Imran Ali Khan
  • Syed Sajid Hussain
  • Faisal Rehman
  • Sajid Siraj
  • Raheel Nawaz

Abstract

Recommender Systems (RS) are widely used to help people or group of people in finding their required information amid the issue of ever-growing information overload. The existing group recommender approaches consider users to be part of a single group only, but in real life a user may be associated with multiple groups having conflicting preferences. For instance, a person may have different preferences in watching movies with friends than with family. In this paper, we address this problem by proposing a Hybrid Two-phase Group Recommender Framework (HTGF) that takes into consideration the possibility of users having simultaneous membership of multiple groups. Unlike the existing group recommender systems that use traditional methods like K-Means, Pearson correlation, and cosine similarity to form groups, we use Fuzzy C-means clustering which assigns a degree of membership to each user for each group, and then Pearson similarity is used to form groups. We demonstrate the usefulness of our proposed framework using a movies data set. The experiments were conducted on MovieLens 1M dataset where we used Neural Collaborative Filtering to recommend Top-k movies to each group. The results demonstrate that our proposed framework outperforms the traditional approaches when compared in terms of group satisfaction parameters, as well as the conventional metrics of precision, recall, and F-measure.

Suggested Citation

  • Yasher Ali & Osman Khalid & Imran Ali Khan & Syed Sajid Hussain & Faisal Rehman & Sajid Siraj & Raheel Nawaz, 2022. "A hybrid group-based movie recommendation framework with overlapping memberships," PLOS ONE, Public Library of Science, vol. 17(3), pages 1-28, March.
  • Handle: RePEc:plo:pone00:0266103
    DOI: 10.1371/journal.pone.0266103
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    References listed on IDEAS

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    1. Olamide Jogunola & Bamidele Adebisi & Khoa Van Hoang & Yakubu Tsado & Segun I. Popoola & Mohammad Hammoudeh & Raheel Nawaz, 2022. "CBLSTM-AE: A Hybrid Deep Learning Framework for Predicting Energy Consumption," Energies, MDPI, vol. 15(3), pages 1-16, January.
    2. Sehrish Iqbal & Saeed-Ul Hassan & Naif Radi Aljohani & Salem Alelyani & Raheel Nawaz & Lutz Bornmann, 2021. "A decade of in-text citation analysis based on natural language processing and machine learning techniques: an overview of empirical studies," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(8), pages 6551-6599, August.
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