Offline shop recommendation method, device and system and storage medium

Abstract

The invention relates to an offline store recommendation method, device and equipment, and a storage medium. The method comprises the steps of obtaining historical consumption records of offline shopping mall consumers and high-noise space-time frame frequency data of the consumers walking in a shopping mall; obtaining a first recommendation set by using a collaborative filtering algorithm according to the historical consumption record; fusing the high-noise space-time frame frequency data to form a consumer walking track, and calculating a transition probability matrix of consumers among shops based on the consumer walking track; obtaining a second recommendation set according to the transition probability matrix and the current position of the consumer; and combining the first recommendation set and the second recommendation set, and performing calculation in combination with a preset distance radius to obtain a final recommendation set. By adopting the method, personalized recommendation of offline shops can be realized.

Description

technical field [0001] The present invention relates to the technical field of information processing, in particular to an offline store recommendation method, device, system and storage medium. Background technique [0002] With the improvement of living standards, people's purchasing power has gradually increased. At the same time, with the rapid increase in the number and types of products, consumers need to spend a lot of time to find the products they want to buy. This process of browsing a large amount of irrelevant information and products will undoubtedly cause consumers who are submerged in the problem of information overload to continue to lose. From this personalized recommendation came into being. Personalized recommendation refers to recommending information and products of interest to users based on their interests and purchasing behavior. Personalized recommendations reduce consumers' information overload and provide high-quality user experience by providin...

AI Technical Summary

Problems solved by technology

However, the currently commonly used personalized recommendation algorithms are mainly based on online multi-dimensional data to generate recommended content, which is difficult to apply to the application scenarios of offline shopping malls.

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