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

Abstract

The invention provides a cloud exhibition content recommendation method based on a generative adversarial network. The method comprises the steps of 1, constructing a generator which is used for generating a score sequence of user browsing behaviors, 2, constructing a discriminator, and carrying out authenticity judgment on the score sequence to obtain effective user browsing behavior data, and 3, recommending unique effective user browsing behavior data as a target result for determining output. According to the method, the user characteristics and the content characteristics are modeled, the cold start recommendation is provided according to the registration characteristics of the new users, the recommendation based on the user characteristics is provided by synchronously utilizing the characteristic similarity of different users, and the recommendation is performed by utilizing the content characteristics to reflect the user preferences from multiple angles; therefore, more latitude combination display is brought to the user, and interaction is carried out to obtain the feedback of the user.

Description

Technical field [0001] The present invention relates to the digital recommendation of the digital recommendation of the online cloud show, which is specifically a method, system, and equipment based on the generation of cloud show content, system, and equipment. Background technique [0002] The cloud exhibition system is a exhibition project in accordance with the number of network technology platforms, unlimited time, venue, number of goods, using text, images, and video. Its essence is based on the Internet, which will construct a digital information integration display space based on the various entities of the cloud computing, big data, mobile Internet technology, social community, and exhibition industrial chain, thereby forming a new exhibition of all-round stereo. Mode, this is also an extension and supplement of the line exhibition. It is a derivative of the derivative of the line entity exhibition. It is known as "never-ending exhibition", the digital function of the cl...

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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