A fashion clothing collocation recommendation method based on user dynamic interest analysis
A clothing and user technology, applied in the field of machine learning, can solve problems such as inability to recommend
Active Publication Date: 2022-05-20
SUN YAT SEN UNIV
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[0011] The disadvantage of the product recommendation method based on user ratings is that the latent factor model is used to discover the diversified interests of users from a single rating, and to mine the information of multiple characteristics of the product, which is more in line with practical applications, and the introduction of negative samples makes the user's interests more differentiated. Large, the quality of the recommendation results is higher, and it can better meet the needs of users, and can be applied to product recommendation
However, it cannot recommend matching and alternative products based on the user's existing products or interests
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[0024] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.
[0025] figure 1 is a flow chart of the fashion clothing collocation recommendation method in the embodiment of the present invention, such as figure 1 As shown, the method includes:
[0026] S1. Obtain clothing product information (including user ratings and product pictures) from the network and clothing product database, as well as label information for categorizing clothing pictures to form a photo library;
[0027] S2, select 10,000 pieces of da...
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The invention discloses a fashion clothing collocation recommendation method based on user dynamic interest analysis. The present invention first establishes a tree structure for the characteristic attributes of clothing products, and then decomposes the "user-commodity" scoring matrix into a "user-implicit feature" matrix and an "item-implicit feature" matrix according to the time factor set by the user And extract the corresponding keywords, and then construct feature vectors according to the high-frequency words and low-frequency words that appear respectively, calculate the probability and sort to obtain a ranking model, and form a ranking list of items based on the ranking model to recommend to users. The present invention can accurately predict the user's interest in clothing collocation within a certain time range according to the user's long-term hobbies and short-term interest drift, and can accurately recommend the user's favorite clothing according to the user's purchase records and ratings on items and matching accessories or accessories.
Description
technical field [0001] The invention relates to the field of machine learning, in particular to a fashion clothing collocation recommendation method based on user dynamic interest analysis. Background technique [0002] Recommender systems are software tools and techniques that suggest useful items to users. They are mainly aimed at those who lack sufficient personal experience and ability to evaluate a potentially large number of optional items. Therefore, recommender systems are needed to make recommendations for each user. Recommendation, since the recommendation system is usually personalized, different users or user groups receive different suggestions, so it is necessary to make personalized recommendations for users. [0003] The simplest form of personalized recommendations is to provide an ordered list of items. From this ranked list, the recommender system attempts to predict the most suitable item given the user's preferences and other constraints. To accomplish...
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IPC IPC(8): G06Q30/06
CPCG06Q30/0631
Inventor 王建峰王若梅苏卓周凡林淑金
Owner SUN YAT SEN UNIV