Product recommendation method and device, electronic equipment and computer readable storage medium

Abstract

The invention relates to the big data technology, and discloses a product recommendation method comprising the following steps: screening according to a first product set and a second product set containing to-be-recommended products, thus obtaining a target product sequence; constructing a first product scoring sequence of each user in the first user set and a second product scoring sequence of each user in the second user set according to the target product sequence; encrypting the similarity of the users in the two user sets according to the first product scoring sequence and the second product scoring sequence to obtain a user similarity set; performing recommendation priority value calculation and screening according to the user similarity set and the second user set to obtain a recommendation user sequence; and recommending the to-be-recommended product according to the recommendation user sequence. The invention also relates to a blockchain technology. The recommended user sequence can be stored in the blockchain. The invention further provides a product recommendation device, electronic equipment and a computer readable storage medium. According to the invention, the flexibility and convenience of product recommendation can be improved.

Description

technical field [0001] The present invention relates to the field of big data technology, in particular to a product recommendation method, device, electronic equipment and computer-readable storage medium. Background technique [0002] With the rapid development of big data and e-commerce, the recommendation system has become an important tool for enterprises to improve their market competitiveness. However, when a new product is added, there is no historical rating data for the product in the system, so it cannot be based on traditional algorithms. Calculating the similarity between users makes it impossible to make recommendations for users. [0003] The currently feasible method is to use the data of the other party for product collaborative recommendation. For example, if product F is newly added to platform A, but product F has been sold on platform B for a period of time, the data in platform B can be used to recommend products to users in platform A. Product recomme...

AI Technical Summary

Problems solved by technology

[0002] With the rapid development of big data and e-commerce, the recommendation system has become an important tool for enterprises to improve their market competitiveness. However, when a new product is added, there is no historical rating data for the product in the system, so it cannot be based on traditional algorithms. Calculating the similarity between users makes it impossible to make recommendations for users

[0003] The currently feasible method is to use the data of the other party for product collaborative recommendation. For example, if product F is newly added to platform A, but product F has been sold on platform B for a period of time, the data in platform B can be used to recommend products to users in platform A. Product recommendation, but this method will leak data privacy, it is only suitable for product collaborative recommendation of two platforms sharing data, the scope of application is narrow, and it is not conducive to promotion

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