Online and offline consumer commodity recommendation method and system based on big data

Abstract

The invention relates to an online and offline consumer commodity recommendation method and system based on big data, and the method comprises the steps: obtaining a trigger instruction which selects a preset offline consumer commodity shopping recommendation option, and calling a pre-stored Apriori algorithm based on the trigger instruction to obtain online commodity purchase type data and online commodity consumption amount data; generating offline commodity shopping recommendation demand data according to the online commodity purchase type data and the online commodity consumption amount data, wherein the offline commodity shopping recommendation demand data at least comprises offline shopping recommended commodities and recommended commodity shop path addresses; and generating an offline commodity recommendation interface. In order to improve the convenience, the path address of the recommended commodity shop is generated, so that the consumption subject can quickly and efficiently obtain the related shop address, and the use experience of the shopping consumption subject can be improved while the recommendation accuracy of the consumption commodity is improved.

Description

technical field [0001] The present application relates to the technical field of consumer commodity recommendation, in particular to a method and system for online and offline consumer commodity recommendation based on big data. Background technique [0002] Big data is a collection of data characterized by large capacity, multiple types, fast access speed, and high application value. Storage and association analysis, from which new knowledge is discovered, new value is created, and new capabilities are enhanced. A new generation of information technology and service formats. Big data must adopt a distributed architecture to carry out distributed data mining on massive data, so it must rely on cloud computing's distributed processing, distributed database and cloud storage, and virtualization technology. [0003] With the development of big data technology, it has been gradually applied to the recommendation of consumer products. For example, in the patent document with app...

AI Technical Summary

Problems solved by technology

[0004] Although the above technical solution can effectively reduce the memory usage of the push data by uniformly compressing the uploaded push data, and confirm whether to delete the push data with a small number of likes according to the number of likes of the user, it can be very good. Free up space in the system memory to improve the user's retrieval efficiency for recommended data, but it is the same in many technologies and cannot achieve efficient and accurate recommendations. For example, it cannot achieve accurate recommendations for online and offline consumer products

[0005] Obviously, the current recommendation method based on big data has the problem of inefficiency

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