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Automatic Film Label Acquisition Method Based on Improved Neural Networks Optimized by Mutation Ant Colony Algorithm.

Junjie Liu1

  • 1School of Art & Design, Henan Finance University, Zhengzhou, Henan 450000, China.

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Summary

This study introduces an improved neural network with a mutation ant colony algorithm for automatic film labeling. The enhanced algorithm improves upon traditional methods and ant colony algorithms for more effective movie recommendation systems.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Evolving public aesthetic standards drive the emergence of new film types and themes.
  • Traditional neural networks face challenges like weight determination, slow convergence, and local minima.
  • Ant colony algorithms alone have limitations that can be addressed with hybrid approaches.

Purpose of the Study:

  • To propose an improved neural network optimized by a mutation ant colony algorithm for automatic film label acquisition.
  • To overcome the limitations of traditional neural networks and standalone ant colony algorithms in film analysis.
  • To enhance movie recommendation systems by improving the accuracy of predicting user ratings and selecting relevant neighbors.

Main Methods:

  • An improved neural network architecture is developed, optimized using a mutation ant colony algorithm.
  • Gradient information from a quantum genetic algorithm neural network is integrated to enhance the ant colony algorithm's performance.
  • User similarity judgment and movie tag weights are incorporated into neighbor selection for rating prediction.

Main Results:

  • The proposed algorithm effectively acquires film labels, addressing issues of weight determination and convergence speed.
  • Integration of user similarity and movie tag weights improves the accuracy of predicting target movie ratings.
  • Experimental results demonstrate the overall effectiveness and superiority of the enhanced algorithm.

Conclusions:

  • The hybrid approach combining neural networks, mutation ant colony algorithm, and quantum genetic algorithm offers a robust solution for automatic film labeling.
  • The refined neighbor selection process enhances the precision of movie recommendation systems.
  • This research contributes to more sophisticated content analysis and personalized recommendations in the film industry.