Electronic commerce recommending method based on support vector machine (SVM)

Abstract

The invention discloses an electronic commerce recommending method based on a support vector machine (SVM). The method includes the steps that a user evaluation predication model based on the SVM is established, textual characteristics including semantics are extracted based on machine learning, evaluations are expressed as multi-dimensional characteristic vectors, the evaluations are classified, an SVM classifier divides the commodity evaluations into useful and useless categories, and the evaluations are automatically recognized; according to useful scores of the evaluations in the classifier, a user item matrix is filled by the adoption of a method of scoring predication through training samples according to the scores; according to importance of all items, corresponding vectors of a kernel function are endowed with corresponding weights, and meanwhile the weights of the corresponding vectors are corrected according to user process behaviors so that the purposes of improving predication precision and achieving an ideal recommending effect can be achieved. Statistics, machine learning, intelligent mode recognition and classification and other technologies are used for analyzing customer electronic commerce access behaviors and the commodity evaluations, commodities which a customer is interested in are predicated through the mode, a recommending result is generated and recommended to the customer, and the customer is helped to rapidly and accurately find out commodities really needed in time.

Description

technical field [0001] The invention designs an e-commerce recommendation method, and in particular relates to an e-commerce recommendation method based on a 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. all use e-commerce recommendation systems to varying degrees. Various Web sites also support recomm...

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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