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Movie Recommender Systems: Concepts, Methods, Challenges, and Future Directions.

Sambandam Jayalakshmi1, Narayanan Ganesh1, Robert Čep2

  • 1Department of Computer Science and Engineering, Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College, Chennai 600 062, India.

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This review systematically examines movie recommender systems, detailing algorithms like K-means clustering and metaheuristic approaches. It highlights performance metrics, challenges, and future research directions for better movie suggestions.

Keywords:
K-meansfiltering techniquesmetaheuristicsmovie recommenderperformance metrics

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Information Retrieval

Background:

  • Movie recommender systems aim to provide personalized user suggestions based on preferences.
  • Effective systems require matching movies with high similarity and performance.
  • Advancements in machine learning have significantly impacted recommender system development.

Purpose of the Study:

  • To conduct a systematic literature review of movie recommender systems.
  • To identify and analyze filtering criteria, algorithms, performance metrics, and implementation challenges.
  • To provide recommendations for future research in the field.

Main Methods:

  • Systematic literature review methodology.
  • Analysis of popular machine learning algorithms including K-means clustering, Principal Component Analysis (PCA), and Self-Organizing Maps with PCA.
  • Emphasis on metaheuristic-based recommendation systems.

Main Results:

  • Detailed discussion of various algorithms and their application in movie recommendations.
  • Identification of key performance measurement criteria.
  • Highlighting current challenges in implementing effective recommender systems.

Conclusions:

  • The study synthesizes current advancements in movie recommender systems.
  • It identifies critical areas for improvement to overcome implementation challenges.
  • The findings offer valuable insights for researchers and data scientists in recommender systems.