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

• Published in: Computational intelligence and neuroscience Vol. 2022; pp. 1 - 10
• Main Author: Yang, Baojuan
• Format: Journal Article
• Language: English
• Published: New York Hindawi 10.08.2022; John Wiley & Sons, Inc
• Subjects: Algorithms; Analysis; Artificial intelligence; Avant-garde; Bionics; Browsing; Classification; Clothing and dress; Decision making; Decision theory; Decision trees; Deep learning; Design; Entropy; Fashion models; Feature extraction; Image classification; Machine learning; Methods; Neural networks; Object recognition; Recommender systems
• ISSN: 1687-5265; 1687-5273; 1687-5273
• DOI: 10.1155/2022/5745457

Most clothing recommendation methods have problems such as high resource consumption and inconsistent subjectively labeled clothing labels. Based on this, a multilabel classification algorithm based on deep learning (DL) theory is introduced, based on which the clothing style recognition model is constructed. Next, the concept of the decision tree algorithm is given, and the clothing recommendation model is built based on this algorithm. Moreover, the clothing style recognition model based on a multilabel classification algorithm and the clothing recommendation system based on a decision tree algorithm are tested by building simulation experiments and combining neural network technology. Finally, the application of the decision tree algorithm and DL theory in clothing recommendation design is studied through the literature collection method. The research focus is to realize the recognition of clothing through decision tree algorithm and DL method to achieve the intelligent recommendation of clothing style. The results show that: (1) the neural network technology in DL theory can realize efficient recognition and classification of clothing style by automatically extracting image features and combining with a multilabel classification algorithm. (2) The decision tree algorithm can make an initial recommendation according to users’ style preferences, then make implicit recommendations through user retrieval, browsing, and other operations, and make dynamic clothing style recommendations to users. (3) When the neural network based on a multilabel classification algorithm is trained, the precision, recall rate, and F1 values are 0.73, 0.43, and 0.55, respectively. (4) After using the clothing recommendation system based on the decision tree algorithm, the subjects’ average satisfaction is 86.25%, indicating that this system can give users a better clothing recommendation experience. This exploration aims to provide a crucial reference for further improving the quality of clothing recommendation services. It has important theoretical significance and practical value for the development of artificial intelligence in the field of fashion design, and is expected to provide a reference for the development of bionics.