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Content-based Document Recommender System for Aerospace Grey Literature: Experimental Testing and User Opinion Survey.

机译:基于内容的航空航天灰色文献推荐系统:实验测试和用户意见调查。

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摘要

The study aims to test content-based document recommender system (CODORS) with sample data to retrieve most relevant technical documents without necessarily matching title terms and closely related to particular search term(s). The CODORS system was put open for users to search and obtain recommendations with weighted relevance ranking and also allowed to compare the results obtained through general OPAC search engine for the same keywords. Based on the findings of the experimental testing and evaluation, some conclusions have been drawn: The results exhibited that the CODORS search provided many more relevant documents and increased the recall value as compared to general OPAC search and also revealed documents that were retrieved for a given query through OPAC search appeared at different places-top, middle or end of the ranked list of documents - generated through the CODORS search for the same query. [ABSTRACT FROM AUTHOR]
机译:该研究旨在使用样本数据测试基于内容的文档推荐器系统(CODORS),以检索最相关的技术文档,而不必匹配标题术语并与特定搜索术语紧密相关。 CODORS系统向用户开放,以搜索和获取具有加权相关性排名的推荐,还允许比较通过通用OPAC搜索引擎针对相同关键字获得的结果。根据实验测试和评估的结果,得出了一些结论:结果表明,与普通的OPAC搜索相比,CODORS搜索提供了更多相关文件,并增加了召回价值,并且还揭示了针对给定条件检索的文件通过OPAC搜索进行的查询出现在文档排名列表的顶部,中间或结尾的不同位置-通过CODORS搜索针对同一查询生成。 [作者的摘要]

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