Electronic commerce recommendation method based on SVM (Support Vector Machine)

Abstract

The invention discloses an electronic commerce recommendation method based on a support vector machine (SVM), which relates to the technical field of electronic commerce recommendation and comprises the following steps of: screening electronic commerce commodities in advance to obtain commodity popularity information of a current period and user information based on a commodity popularity order; and obtaining user information based on a commodity popularity order, calculating the similarity between the to-be-recommended objects by using an association recommendation algorithm based on the user screening evaluation information, the obtained effective information and the user item matrix, and taking an obtained prediction result as a recommendation result. According to the method, the problem of user data sparsity is solved, the optimal prediction scoring effect is achieved, the commodities with high scores are recommended to the users through the user information based on the commodity popularity orders, and the recommendation accuracy and the commodity popularity are improved.

Description

technical field [0001] The present invention relates to the technical field of e-commerce recommendation, in particular, to a method for e-commerce recommendation based on support vector machine SVM. Background technique [0002] With the rapid development of the Internet and e-commerce, while e-commerce brings infinite convenience to users, with the rapid increase of information, information overload also makes the whole system more complicated, and users cannot find the product information they need to find smoothly. , the e-commerce recommendation system can effectively dynamically capture user needs and preferences, predict possible user preferences, recommend products that they may be interested in, and successfully complete the entire shopping process. E-commerce recommendation systems have good development and application prospects. At present, Amazon, Dangdang, eBay, Taobao, etc. have used e-commerce recommendation systems to varying degrees. Various Web sites also s...

AI Technical Summary

Problems solved by technology

[0008] The traditional collaborative filtering recommendation technology has the problem of low recommendation quality due to the low rating or invalid data of unrated products and invalid ratings. A variety of solutions are proposed for this situation, including matrix filling, matrix dimensionality reduction and other technologies

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