Research And Implementation Of Clothing Matching Recommendation Algorithm Based On Deep Learning

With the rapid development of the Internet industry,the Internet is becoming increasingly popular in China,more and more people choose to shop online.Apparel industry occupies a very large market share,and the apparel industry with more attention to the problems of clothing matching.Most users do not know how to match clothes,there is great need for a clothing matching expert to carry out proposal to the common users during they buy clothes online.In order to meet the above requirements,this thesis,from the point of machine learning,try to build clothing matching recommendation algorithm to replace the clothing matching experts to provide users with professional clothing matching advices.The thesis,from the clothing text description data,the clothing image data,the user purchase behavior data and clothing matching data given by experts,carries on the analysis to the clothing matching problem,mainly studies the up and down clothes matching recommendation problem.According to the clothing matching data given by the experts,the thesis proposes a collaborative recommendation algorithm to explore more clothing matching combinations.The test results show that the proposed clothing matching collaborative recommendation algorithm is more effective.The vector space model is used to represent clothing text description,and then the clothing matching prediction is carried out by calculating the similarity degree.Based on the data of user purchase behavior,the thesis explores the clothing matching pattern by association rules mining.The thesis mainly focuses on the study of clothing image data,and applies the deep convolutional neural network to excavate the image information of the clothing,and transforms the clothing matching problem to the binary classification problem.In this thesis,the TCNN-GBT model is proposed.Due to the size of training sample data too small,the idea of transfer learning is used to transfer the deep convolutional neural network model trained in ImageNet data to the clothing matching field,and the parameters of network are fine-tuned.Based on the migrated model,gradient boosting tree algorithm is added to supervise learning.Meanwhile,the thesis improves on the original gradient boosting tree algorithm,and proposes a new objective function,and theoretically analyzes and validates the algorithm improvement.The test results show that the proposed TCNN-GBT is effective.In the end,the thesis studies the ensemble strategy of several kinds of clothing matching algorithms,proposed a hybrid recommendation algorithm based on waterfall fusion method and supervised learning fusion method.The test results show that the hybrid recommendation algorithm has a certain improvement over the single algorithm.