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Personalized Recommendation Algorithm for Movie Data Combining Rating Matrix and User Subjective Preference.

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This study introduces a Non-negative Matrix Factorization (NMF) personalized movie recommendation algorithm to address inefficient movie searching. The NMF algorithm significantly improves recommendation accuracy and user satisfaction, offering a better user experience.

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Area of Science:

  • Computer Science
  • Information Retrieval
  • Machine Learning

Background:

  • The proliferation of online movie resources necessitates efficient user recommendation systems.
  • Current movie searching methods are time-consuming and inefficient for users.
  • Personalized recommendation algorithms are crucial for enhancing user experience in the digital entertainment industry.

Purpose of the Study:

  • To propose and evaluate a Non-negative Matrix Factorization (NMF) personalized movie recommendation algorithm.
  • To enhance movie discovery by leveraging users' historical behavior and preferences.
  • To compare the performance of the proposed NMF algorithm against existing methods.

Main Methods:

  • Analysis of user movie review data to understand commenting patterns.
  • Comparative experiments evaluating different dimensions of the NMF algorithm (NMF-A vs. NMF-E).
  • Testing the effectiveness of the improved NMF personalized recommendation algorithm using Mean Absolute Error (MAE) and recall metrics.
  • User satisfaction surveys to assess the practical impact of the recommendation system.

Main Results:

  • A significant portion of users post few reviews (48.42% post one, 79.76% post <=5).
  • The NMF-E algorithm demonstrated superior performance in accuracy, recall, and F1-score compared to NMF-A.
  • The improved NMF algorithm achieved a lower MAE (0.83 vs. 0.837) and higher recall (up to 0.200) than unimproved versions.
  • User satisfaction increased by 30% and dissatisfaction decreased by 7% after implementing the NMF algorithm.

Conclusions:

  • The NMF-E algorithm is the most effective among the tested NMF variants.
  • The improved NMF personalized recommendation algorithm offers greater accuracy and better recall.
  • The NMF personalized recommendation algorithm significantly enhances user satisfaction and meets user needs.