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Clothing is an important commodity category for e-commerce sites. As the number of consumers and clothing on the e-commerce site continues to grow, the "sparsity" and "cold start" issues of consumer rating data have affected the accuracy of collaborative filtering recommendation algorithms. To solve the above problems, an improved collaborative filtering algorithm is proposed. Based on the classification attributes of clothing, the algorithm weights and combines the clothing category preference similarity and consumer feature similarity to obtain a comprehensive similarity and uses this to conduct the final personalized recommendation. Experiment results show that the algorithm not only optimizes the selection of nearest neighbors, but also alleviates the problem of data sparsity, and achieves good recommendation results.
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