User attribute scoring guide-based personalized recommendation system and recommendation method thereof

Abstract

The invention discloses a user attribute scoring guide-based personalized recommendation system and a recommendation method thereof. The system comprises a data preprocessing module used for performing crawling preprocessing on network text contents and performing keyword tag marking on a preprocessed text, a user interaction module used for collecting historical browsing information and scoring information of a user to generate a user attribute document and a scoring matrix, a similar neighbor user search module used for building a latent Dirichlet distribution model according to the attribute document corresponding to historical data of the user, and an interest distribution mining module used for mining interest distribution of the user in combination with Pearson similarity and latent Dirichlet distribution according to neighbor users. By utilizing a scoring guide-based latent Dirichlet distribution method, the scoring accuracy is improved; and the text can not only assist the user and a content platform to manage massive text contents, but also improve the accuracy of an existing system in numerous application scenes of new media, e-commerce and the like.

Description

technical field [0001] The invention relates to the fields of computer application technology and e-commerce technology, in particular to a personalized recommendation system and a recommendation method based on user attribute ratings. Background technique [0002] With the development of information technology and the Internet, people have gradually entered the era of information overload from the era of information scarcity. In this era, both ordinary users as information consumers and content providers or product providers as information producers have encountered great challenges. As a user, how to find the information of interest from a large amount of information is a very difficult thing. As a provider, how to make the information produced by itself stand out and be welcomed by the majority of users is also a very difficult thing. Therefore, many researchers and companies have developed recommender systems to resolve this contradiction. The task of the recommendati...

AI Technical Summary

Problems solved by technology

Not conducive to rapid development of technology

In CN105447179A, the fast recommendation in the text field is realized, and the potential interest of the user is insufficiently mined

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