Commodity recommendation method based on countermeasure network, electronic device and storage medium

Abstract

The invention relates to a commodity recommendation method based on antagonistic network, an electronic device and a storage medium, comprising: obtaining behavior data of a user to a commodity, wherein the behavior data comprises purchase data and browse data; obtaining the behavior data of the user to the commodity; obtaining the behavior data of the user to the commodity. Generating a random input vector according to the behavior data; Inputting a random input vector and behavior data into a countermeasure network model, the countermeasure network model comprising a generator and a discriminator, wherein the random input vector input generator obtains a randomly generated vector, the randomly generated vector is used as a virtual input of the discriminator, and the behavior data is usedas a real input of the discriminator; judging the true input and the virtual input by The discriminator, judging the ratio of the true input and the virtual input by output, and judging whether thediscriminator converges or not. When the discriminator does not converge, the generator is driven to update until the discriminator converges, and the randomly generated vector of the generator is used as a recommendation sequence. The method, apparatus and medium accurately learn the characteristics of the recommending entity.

Description

technical field [0001] The present invention relates to the technical field of commodity recommendation, and more specifically, relates to a commodity recommendation method based on an adversarial network, an electronic device and a storage medium. Background technique [0002] With the development of network technology, more and more users browse and purchase products through the Internet, APP, etc. In order to facilitate users to purchase products, products that they may like can be recommended to users. [0003] At this stage, single-domain recommendation systems are mainly implemented using traditional Bayesian prior / collaborative filtering algorithms or deep learning network models such as CNN / RNN / DBN. improve. Contents of the invention [0004] In view of the above problems, the object of the present invention is to provide a recommendation method based on a generative adversarial network for accurately learning features of recommended entities, an electronic device...

AI Technical Summary

Problems solved by technology

[0003] At present, single-domain recommendation systems are mainly implemented using traditional Bayesian priori / collaborative filtering algorithms or deep learning network models such as CNN / RNN / DBN. improve

Images