An expert recommendation method based on multi-source information fusion technology

Abstract

The invention discloses an expert recommendation method based on multi-source information fusion technology, including: crawling technical experts' scientific papers, invention patents, fund project information and Web page information to construct a knowledge base, and constructing a keyword dictionary according to the keyword field of the knowledge base keywords; extract the author field of the knowledge base for word frequency co-occurrence analysis, and build a subnetwork of expert cooperation; use regular expressions and named entity recognition algorithms to extract the research direction and personal information of web page experts to build a Web subnetwork; Extract document-subject and subject-keywords from the abstract field of the library, and use the TF-IDF algorithm to extract the five words with the largest weight in the abstract field to construct a topic subnetwork; combine three subnetworks with expert name-institution as constraints to construct and calculate expert information network In the expert centrality value, sort the expert centrality value and recommend the top 5 experts as the recommendation result.

Description

technical field [0001] The invention belongs to the field of multi-source information fusion and expert recommendation, in particular to an expert recommendation method based on multi-source information fusion technology. Background technique [0002] Traditional technical expert recommendation algorithms usually use a single data source for recommendation. The single data source recommendation is easily constrained by the data source, and it is easy to cause the lack of expert information, which makes it impossible to fully display expert information and expert recommendation. If the relationship with the working institution cannot be effectively expanded, researchers can only recommend expert information with one attribute, while the multi-source information fusion method can integrate the multi-dimensional attributes of experts according to the constraints, so as to fully display the technical expert information, and The expert cooperation relationship subnetwork, Web sub...

AI Technical Summary

Problems solved by technology

[0007] Existing expert recommendation patent applications include: Su Yurong, Li Zhenhua, an expert recommendation method and system: CN111160699 A, 2020.05.15, although it can be implemented on the basis of multiple recommendation systems to give users more standard recommendation results, there may be The problem of information redundancy, and the problem of missing information cannot be avoided; Zhang Yongfeng, Tan Xinyu, Li Zhenhua, an expert recommendation method and system based on multiple data sources: CN 111008330 A, 2020.04.14, by adding the score field to the expert and according to the score field The corresponding value sorts the experts and generates recommendation results. Although the patent involves multi-source data, it only scores the expert fields and fails to mine the hidden expert relationship between multiple data; Wang Jian, Sun Jiqing, Lin Hongfei, a A community Q&A expert recommendation method based on a recurrent neural network: CN 108021616 A, 2018.05.11. This recommendation method can effectively represent sentence grammar and semantic information, reducing manual intervention on recommendation results, but it is easily affected by the original corpus and thus Recommended results have an impact

[0008] Although the above patents have effectively improved the recommendation results, they do not involve the recommendation of expert relationships and regional relationships. They only recommend from the experts themselves, and cannot comprehensively consider factors such as regional relationships and cooperative relationships, resulting in invalid recommendation results and cannot be applied. to actual recommendation

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