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  • 标题:A Method for the Recommendation of Similar Documents and Related Researchers in National R&D Information Collections
  • 本地全文:下载
  • 作者:Heejun Han ; Heeseok Choi ; Jaesoo Kim
  • 期刊名称:International Journal of Software Engineering and Its Applications
  • 印刷版ISSN:1738-9984
  • 出版年度:2016
  • 卷号:10
  • 期号:1
  • 页码:119-128
  • DOI:10.14257/ijseia.2016.10.1.12
  • 出版社:SERSC
  • 摘要:The users of academic databases and R&D information often use search services to obtain necessary data for their studies. Most web users create various search queries and request these needed information from the system, and as a final destination of searching selected information lists and obtaining information, users are moved to the detailed page of the corresponding information. Similarly, in cases of academic information services providing journal and patent information, the final destination of the user is the specific metadata information page or the full article page, and in this case, providing other similar types of academic information and the names of researchers in other related fields is essential for satisfying the information requirements of the users. In case of the NTIS (National Science and Technology Information Service), it provides search services on national R&D information (tasks, participating personnel, research products, facilities and equipment, etc.), but lacks provision of similar documents within the same DB or among different DBs. In this article, the authors explain user queries and search items provided by the search engine, serviced by the NTIS for data categorized as research products including journals, patents, research reports, and trend analyses, and search service methods for listing similar documents within the same or different contents, and names of researchers in related fields using boosting technologies. This way, the R&D information desired by the user may be efficiently provided in the final service screen, which can reduce repeated efforts on searching.
  • 关键词:Similar Document Retrieval; Related Researcher Recommendation; NTIS; ; R&D Information
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