Commodity association degree determination method and device

Abstract

The invention provides a commodity association degree determination method and device, and relates to the technical field of electronic commerce. The method comprises the following steps: according tothe category purchase data and the user non-purchase behavior data, obtaining an association relationship corresponding to a category purchase behavior of the purchase target category and a user non-purchase behavior and a category purchase behavior of other commodity purchase categories; and based on the association relationship and the weights corresponding to the category purchasing behaviorsand the user non-purchasing behaviors, determining the association degree between the commodity purchasing categories, and selecting the associated categories according to the association degree. According to the method and the device, user behavior data of browsing, shopping cart adding, attention, purchasing and the like are comprehensively analyzed. The product association degree is calculatedthrough the quantifiable association coefficient, the storage space can be saved, the calculation speed can be ensured, the method can be applied to different commercial marketing scenes, accurate product positioning analysis can be met, marketing activities with high recall rate can be met, and the use experience of users can be improved.

Description

technical field [0001] The present disclosure relates to the technical field of e-commerce, and in particular to a method and device for determining the degree of commodity association. Background technique [0002] At present, the Internet e-commerce business is developing rapidly, and the categories of commodities involved have already tens of thousands. Among the many categories, how to accurately identify and analyze highly related categories is crucial in precision marketing, optimal combination of commodities, and cross-category promotion. In business scenarios, it plays a vital role. At present, there are two main calculation methods for category correlation: one is based on association rule analysis of frequent itemsets in orders, and the other is to directly calculate category correlation based on manually given rules. Traditional correlation calculation methods, such as the Apriori algorithm, require multiple scans of the entire database, resulting in a large amou...

AI Technical Summary

Problems solved by technology

Traditional correlation calculation methods, such as the Apriori algorithm, require multiple scans of the entire database, resulting in a large amount of computing resource overhead and memory overhead. At the same time, many online scenarios are one-to-one rather than many-to-one rules. There will be a lot of rule waste in the calculation

Moreover, the existing category association analysis methods usually only use a single dimension of the order, and cannot comprehensively consider the overall shopping behavior of users

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