Personalized research direction recommending system and method based on themes

Abstract

The invention discloses a personalized research direction recommending system and method based on themes. Paper topics read by users and preference of the users for related paper topics can be obtained through the recommending system according to all the papers read by the users and according to the themes of the papers obtained when training is conducted through a theme model training module, therefore, the recommending system can recommend a new research direction for the users to widen the vision of the users. The innovation key of the personalized research direction recommending system and method based on the themes is to construct a three- layer graph model according to the relationship between the users and the papers and the relationship between the papers and the themes, to calculate preference values of the users for the themes according to the three-layer graph model, to obtain a user-theme preference weight matrix, and to calculate similar user set between the users and other users based on the weight matrix. The preference degree of the themes which are not touched by the users is predicted according to the similarity value of the similar users in the similar user set and according to the preference values of the similar users for the themes, and the research direction, namely, the research theme, is recommended for the users according to the prediction result.

Description

technical field [0001] The present invention relates to a personalized research direction recommendation system and recommendation method. Specifically, it relates to a subject-based personalized research direction recommendation system and recommendation method for user behavior, which belongs to the technical field of data mining and machine learning. Background technique [0002] At present, with the rapid development of the Internet, people are in an era of information explosion. Facing the massive amount of information at this stage, how to realize the screening and filtering of information has become an important indicator to measure the performance of a network system. Every network system with a good user experience will screen and filter massive amounts of information, and present the information that users are most concerned about and most interested in in front of users. This not only greatly increases the work efficiency and effect of the network system, but als...

AI Technical Summary

Problems solved by technology

[0019] Data sparse problem: The scale of the current academic resource recommendation system is getting larger and larger, because the number of papers is often hundreds, thousands, or tens of thousands, and the number of overlapping papers selected by two users is very small

[0020] The problem of less recommended information: the existing academic paper recommendation system usually calculates the similarity between the paper to be recommended and the papers that the user has browsed based on the content of the paper that the user has recorded the operation behavior, and based on the similarity value Recommend it in high and low order

From the user's point of view, such a recommendation system is indeed more accurate, but this is likely to cause users to get some "precise recommendations" with zero information, and make the user's field of vision narrower and narrower

Such an academic paper recommendation system has no practical application value

Images