A Method for Academic Paper Recommendation Based on Deeply Aligned Matrix Factorization Model

Abstract

The invention discloses a deep alignment matrix decomposition model-based academic paper recommendation method. The method comprises the following steps of: respectively mapping a user and a paper andthe paper and a word into low-dimensional feature spaces with a same dimensionality through two nonlinear multilayer sensors; building a bridge between the two nonlinear multilayer sensors through maximizing a similarity between two low-dimensional expression vectors of the same paper; and finally transferring information through the paper information bridge and training the two sensors in turn.Through the method, when the sensor of the user-paper part is trained, information of the paper-word part can be utilized, and when the sensor of the paper-word part is trained, information of the user-paper part can be utilized, so that user-paper collection records and paper content text information can be used at the same time so as to jointly make contribution to user paper recommendation.

Description

technical field [0001] The invention relates to a method for recommending academic papers based on a deep alignment matrix decomposition model. Background technique [0002] At present, with the development of the Internet, there are a large number of academic papers on the Internet, causing researchers to face a serious problem of information excess. It is estimated that as of 2014, there are already tens of billions of academic papers online, and the number is still growing at more than 6,000 every day. How to help researchers and users quickly find articles they may be interested in from such a large number of papers has become a problem that people pay attention to. [0003] Most of the current related work adopts the method based on keyword retrieval, and treats academic papers as ordinary web pages. However, these methods both ignore the structural features of the paper itself, and do not model users' personalization. In recent years, with the rise of social network...

AI Technical Summary

Problems solved by technology

[0008] Purpose of the invention: In order to overcome the problem of "sparse user-paper interaction data" and paper cold start in the existing collaborative filtering recommendation method, the present invention uses a new type of hybrid recommendation algorithm, adding the text content of the paper to the recommendation based on collaborative filtering In the algorithm, the information of two data sources of "user-paper interaction" and "text content of papers" are used at the same time to recommend academic papers of interest to users

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