Community-based authors and their academic paper recommendation system and recommendation method

Abstract

The invention relates to a community-based author and academic paper recommending system and a recommending method. A double-layer quotation network consisting of an author layer and an academic paper layer is formed by utilizing a quotation relation between an author and the academic paper and the community information, then a user interesting model is established according to a historic behavior record of the user and the academic paper set read by the user, finally the user demand is analyzed according to the obtained double-layer quotation network and the user interesting model, and the author and academic paper thereof can be recommended to the user. The system is provided with an academic paper capturing module, an academic paper preprocessing module, a double-layer quotation network establishing module, a user interesting model establishing module and an individualized academic paper recommending module as well as a database. By adopting the recommending system and recommending method, not only can the correlation of the study content among users be used for establishing an author community through a subjective model, but also multiple attribute values of the to-be-recommended author and academic paper inside the community can be calculated, and the weakness that the calculation of the existing recommending algorithm is large can be improved; and meanwhile, multiple attribute values of the author and academic paper can be simultaneously calculated, so that the recommend result is more diversified, and the user requirement can be better met.

Description

technical field [0001] The present invention relates to a system and method for recommending authors and their academic papers, to be precise, to a community-based personalized recommendation system and method for authors and their academic papers, belonging to the technical fields of data mining and machine learning. Background technique [0002] In 2003, Blei et al. proposed the latent Dirichlet distribution LDA (Latent Dirichlet Allocation) topic model. Subsequently, researchers made many improvements to LDA. The mining of academic papers based on the topic model is an important application of the topic model. By mining the topics of the papers, the development and evolution of academic papers can be understood more deeply. In 2004, Michal Rosen-Zvi et al proposed the Author-Topic AT (Author-Topic) model based on LDA. The AT model is to build a text topic model of academic papers from the perspective of the author. For the corpus of academic papers, the LDA model does ...

AI Technical Summary

Problems solved by technology

The system can not only use the correlation of research content among authors, build author community through topic model, solve the problem of poor community construction results due to lack of information; but also calculate the correlation value of authors and papers to be recommended within the community, improve It overcomes the shortcomings of the existing recommendation algorithm with a large amount of calculation; at the same time, by calculating various attribute values ​​​​of authors and papers, the recommendation results are more diverse and more in line with the needs of users

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