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A Movie Recommender System Based on User Profile and Artificial Bee Colony Optimization.

Faezeh Rajabi Kouchi1, Sahar Oftadeh Balani2, Amirhossein Esmaeilpour3

  • 1Department of Computer Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran.

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This study introduces a new movie recommendation algorithm using data mining. The novel approach enhances recommendation precision by 1.39% and recall by 0.8% using optimized feature extraction and user similarity metrics.

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Area of Science:

  • Computer Science
  • Artificial Intelligence

Background:

  • Personalized movie recommendations are crucial for user engagement.
  • Existing recommendation systems face challenges in accurately capturing user preferences.

Purpose of the Study:

  • To propose a novel movie recommendation algorithm.
  • To improve the precision and recall of movie recommendations.

Main Methods:

  • Utilized data mining techniques for user profile preprocessing and feature extraction.
  • Employed the bee colony optimization algorithm for optimal feature selection.
  • Calculated user similarities using extracted features and Euclidean distance for generating recommendations.

Main Results:

  • The proposed algorithm demonstrated an average increase in precision of 1.39%.
  • The algorithm achieved an average increase in recall of 0.8% compared to existing methods.
  • Evaluation was performed using the MovieLens database, validating the algorithm's effectiveness.

Conclusions:

  • The developed algorithm offers a more effective approach to movie recommendation.
  • The method successfully enhances recommendation accuracy through optimized feature selection and similarity measures.