The Analysis And Design Of Expert Recommendation System Of Massive Academic Resources

The pool of experts is one of the most important information support resources inscientific and technological activities. With the rapid development of science andtechnology and the increasing emergence of crossed-subject, the accuracy andcompleteness of expert recommendation will affect the final quality and effect ofpublishing activities, topic planning, research project and other scientific andtechnological activities.In this thesis, the author designed a expert recommendation system aims toprovide knowledge service for various fields through the mining and analysis ofclassification information, key words, topic words and other subject information ofmassive academic information resources based on content, the confirmation of expertidentity, the scientific evaluation of expert’s academic influence and the mining of thecooperation relations between expert authors. The beginning of thesis is about theintroduction of the background and significance of this thesis, the survey of the statusquo of domestic and foreign expert pool research and the study objective and structureof the thesis. The system presents its own definition of expert on the basis of manyexpert definitions and analyzes from the aspects of business, data mining, foreignservice and others of academic resources expert recommendation system, thusobtained the resource integration need of massive academic resources expertrecommendation system, data mining need and foreign service need. After we knowthe needs clearly, the author designed the system from the aspects of generalframework, resource integration, application system, foreign service and securitysystem and presented the establishment method of expert recommendation system ofmassive academic resources. In the thesis, the author took an expert in a field asexample and carried out case study of expert recommendation system based onacademic resources from the aspects of the mining of expert’s specialty, expertidentity identification, relations between experts, etc. The last part is the summary ofthe main research achievements and deficiencies of the thesis.In this thesis, the author started from the actual needs, mined and analyzedclassification information, key words and other subject information of massiveacademic information resources based on content, helped to confirm expert’s identity,mined the cooperation relations between expert authors and designed an expertrecommendation system that can provide knowledge service of multiple fields. Thesystem is achievable; it has been developed in the company’s R&D department andproved feasible in small range.