Cloud exhibition content recommendation method, system and equipment based on generative confrontation network

Abstract

The present invention provides a cloud exhibition content recommendation method based on a generative confrontation network, including the first step, constructing a generator for generating user browsing behavior score sequence; the second step, constructing a discriminator, and verifying the authenticity of the score sequence Judging to obtain valid user browsing behavior data; the third step is to recommend unique valid user browsing behavior data as the target result of the determined output. The present invention provides cold-start recommendations according to the registration characteristics of new users by modeling user characteristics and content characteristics, and simultaneously utilizes the similarity of characteristics of different users to provide recommendations based on user characteristics and uses content characteristics to make recommendations to reflect users from multiple perspectives The way of preference is to achieve a combined display that brings more latitude to users, so as to interact and obtain user feedback.

Description

technical field [0001] The invention relates to the technical field of digital recommendation for an online cloud exhibition system, specifically a method, system and equipment for cloud exhibition content recommendation based on a generative confrontation network. Background technique [0002] The cloud exhibition system relies on the network technology platform to operate an exhibition project with an organic combination of text, images, and videos, regardless of time, venue, number of people, and quantity of goods. Its essence is based on the Internet to build a digital information integrated display space with cloud computing, big data, mobile Internet technology, social communities, and various entities in the exhibition industry chain, thus forming a comprehensive and three-dimensional new exhibition The model, which is also an extension and supplement to offline exhibitions, is a derivative product of offline physical exhibitions. It is known as "the never-ending exhi...

AI Technical Summary

Problems solved by technology

[0009] However, in the user-centered recommendation system, it does not reflect how to interact with users in a natural and transparent way, understand the real needs and preferences of users, and through natural interaction with users, the recommendation system estimates, extracts, and also provides User feedback, affecting the user's satisfaction, preference, demand, interest, activity patterns and other implicit states, so as to start from the user's preference, the behavior process of user decision-making reasoning, and make the best decision for the user.

[0010] At the same time, the goals of optimization embodied in existing technologies are all short-term benefits, such as click-through rate and viewing time, and it is difficult to model long-term benefits; the most important thing is to predict user interest, but the models are all based on logged feedback training , the samples and features are extremely sparse, and a large number of materials have not been fully displayed. At the same time, there are still a large number of new materials and new users pouring in, and there is a large amount of bias. In addition, users' interests change drastically, behaviors are diverse, and there are many noises

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