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Design and Analysis for Fall Detection System Simplification

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

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

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|August 22, 2022

Summary

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.