Personalized news recommendation device and method based on news content and theme feature

Abstract

The invention discloses a personalized news recommendation device and method based on news content and theme feature. The recommendation device is equipped with seven modules, namely a news capturing module, a pre-treatment module, a theme model training module, a theme model predicting module, a user model building module, a news recommendation module and a recommendation treatment module. The recommendation method comprises the following steps: building an personalized user model with the theme model and a relevant named entity noun sequence to express the interest preference of the user reading news, and calculating weight and converting the theme feature vector of users so as to reduce the influence of hot themes and single news content on the user interest, thereby effectively overcoming the defects of concentrated user interest and insufficient diversity of recommendation results. In a recommendation output stage, an initial recommendation news list is treated, a theme grouping process based on the personalized user model is added on the basis of currently repeating data deleting and redundancy filtering, and news texts are reordered again according to the aging weight so as to recommend the accurate, diversified and novel personalized news.

Description

technical field [0001] The present invention relates to a personalized news recommendation device and method, more precisely, to a personalized news recommendation device and method for Internet users based on news content and theme features; it belongs to the technical field of data mining and machine learning. 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. At the same time, both information consumers and information producers have encountered great challenges: for information consumers, it is very difficult to find the information they are interested in from massive amounts of information; for information producers, It is also very difficult to make the information produced by oneself stand out and attract widespread attention from users. The recommendation system or recommendation device is an important tool to solve this con...

AI Technical Summary

Problems solved by technology

[0038] In view of this, the purpose of the present invention is to propose a personalized news recommendation device and recommendation method based on news content and topic features. The features of the present invention include two aspects: using topic models and named entity noun sequences (with news content features Relevance) Build a personalized user model to express the user's interest preference in reading news, and perform weight calculation and conversion on the user's topic feature vector, so as to reduce the influence of popular topics and news content on user interest, and effectively solve the problem of user Concentrated interests and insufficient diversity of recommended results; in the recommendation output stage, the initial recommended news list is processed, and on the basis of existing duplicate data deletion and redundant filtering, the personalized user interest based on the present invention is added. The topic grouping process of the model, and reorder the news texts according to the timeliness weights, to achieve accurate, diverse and novel personalized news recommendations

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