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Clothing Design Style Recommendation Using Decision Tree Algorithm Combined with Deep Learning.

Baojuan Yang1

  • 1Fashion Design Department, Art and Design School, Changchun Humanities and Sciences College, Changchun 130117, Jilin, China.

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This study introduces deep learning (DL) and decision tree algorithms for intelligent clothing style recognition and recommendation. These methods improve accuracy and user satisfaction in fashion design applications.

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Area of Science:

  • Artificial Intelligence
  • Computer Vision
  • Fashion Technology

Background:

  • Existing clothing recommendation systems suffer from high resource consumption and inconsistent, subjective labeling.
  • There is a need for more efficient and accurate methods for clothing style recognition and personalized recommendations.

Purpose of the Study:

  • To develop and evaluate a clothing style recognition model using a multilabel classification algorithm based on deep learning (DL).
  • To construct a clothing recommendation model utilizing the decision tree algorithm for personalized and dynamic suggestions.
  • To investigate the combined application of DL and decision tree algorithms for intelligent clothing recommendation design.

Main Methods:

  • A clothing style recognition model was built using a multilabel classification algorithm derived from deep learning (DL) theory.
  • A clothing recommendation model was developed based on the decision tree algorithm.
  • Simulation experiments combining neural network technology were employed to test both models.

Main Results:

  • Deep learning (DL) neural networks efficiently recognized and classified clothing styles by automatically extracting image features.
  • The decision tree algorithm provided initial recommendations based on user preferences and dynamic implicit recommendations through user interactions.
  • The DL-based neural network achieved precision, recall, and F1 scores of 0.73, 0.43, and 0.55, respectively.
  • The decision tree-based recommendation system resulted in an average user satisfaction rate of 86.25%.

Conclusions:

  • Deep learning (DL) with multilabel classification effectively recognizes clothing styles from images.
  • Decision tree algorithms enable dynamic and personalized clothing recommendations, enhancing user experience.
  • The integration of DL and decision tree algorithms offers significant theoretical and practical value for AI in fashion design and recommendation services.