Multi-platform commodity attribute matching processing method and system

Abstract

The invention discloses a multi-platform commodity attribute matching processing method and system, and the method comprises the steps: firstly obtaining the user data of a multi-network platform, extracting behavior data from the user data, generating a behavior data set, and building a guide relation correlation model between the behavior guide factors of the multi-network platform and the user behaviors according to the behavior data set; determining guided user behaviors generated by the influence of the behavior guiding factors on the user from the association model to obtain a guiding factor domain of each type of guided user behaviors, and judging the behavior state change condition according to the association model and the guiding factor domain; and determining a mapping guide factor of the non-e-commerce platform corresponding to the guide factor domain according to the behavior state change condition, and applying the mapping guide factor to the user. According to the method, the effect of efficient and accurate matching oriented to the target user can be achieved on a non-e-commerce traditional platform according to the commodity attributes.

Description

technical field [0001] This application relates to the technical field of data analysis, and in particular to a multi-platform product attribute matching method and system. Background technique [0002] With the development of big data analysis technology, most e-commerce platforms currently have the function of recommending products to users according to the matching degree of product attributes and users. User data such as purchase records, browsing records, shopping cart placement records, etc., and at the same time analyze the above records of friends according to the user's social relationship, and determine user preferences by analyzing the associated products in these user data, such as the user's preferred products Type, price range, product brand, etc., and then match product attributes with user preferences, recommend products with a high degree of matching to users, and realize the function of pushing products that are more in line with user preferences. [0003]...

AI Technical Summary

Problems solved by technology

[0004] However, when these non-e-commerce platforms realize the above functions, a prominent problem is the lack of user data associated with commodity attributes, because the user data accumulated on these non-e-commerce platforms, such as historical data of multimedia on demand, Short video browsing history data, historical data of joining live broadcast rooms, etc., lack direct correlation with product attributes; The number and frequency of browsing and purchasing of recommended products on e-commerce platforms are relatively low, and it is difficult to push products that meet user preferences to users through user data and corresponding analysis on non-e-commerce platforms

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