On Relevance Criteria And Its Application In Information Retrieval

It has been widely recognized that relevance is one of the basic problem in library and information science. Foreign studies have made abundant achievements theoretically and practically, for more information please consult Saracevic(1975), Mizzaro(1997),Schamber(1994) and Saracevic (2007),et al. Though more and more native scholars are paying attention to Relevance, only some introductions and overviews of foreign research progress can be retrieved from CNKI. In view of these, a serial of user-oriented research on relevance criteria and their application in a native atmosphere are carried on in this article. Primary results and conclusions are listed as follows:(1) Provision of a set of relevance criteria. The paper selected 4 grades in the information management department and 1 grade in education science of Nanjing University as data resources, and provided 9 kinds of relevance criteria including broadcasting features, content, context, use, system features, pleasure, quality, collection and document feature. By analyzing the frequencies of these relevance criteria, it has been found that document feature, quality, content, collection and use accounted for the main location, while pleasure, context, broadcasting feature are not so important. Compared to Schamber's (1991) and Barry's(1993), the paper extends the set of relevance criteria, and parse out three new relevance criteria including document collection, document use and broadcasting feature.(2) Document features which affect the user's relevance judge. By the content analysis to the relevance criteria text, the author parse out 15 document features including references, publishers, publishing time, keyword, funds, space, text, title, document format, abstracts, language, document type, source journals, authors and institutions, which have impacts on user's relevance judge.(3) Impact of task complexity and gender on relevance criteria's selection. Research show that different user have differences in the selection of relevance criteria including document content, document use, document features, author, and document type when are faced to tasks with different complexion.(4) Construction of academic information retrieval system success model oriented to relevance criteria based on relevance criteria, value added model, TEDS model and ISSM as well as validation of its validity. A questionnaire is design based on the model,1114 questionnaires were distributed and 1054 were returned of which 929 questionnaires were valid. By SEM method, we prove that:①System fact 1 and system fact 2 have positive and negative impact on selection, with the path coefficient 0.59 and-0.39 each.②System fact 2, system fact 3 and selection have positive impact on self-adaption, with the path coefficient 0.11,0.15 and0.50 each.③System fact 1 and flexility have positive impact on self-adaption, with the path coefficient 0.14 and 0.70 each.④System fact 5, flexility, perceived time and reliability have positive impact on system performance, with the path coefficient 0.20,0.29,0.10 and 0.70 each.⑤Beauty and play experience have positive impact on perceived emotion, with the path coefficient 0.54 and 0.27 each.⑥System fact 4 and system fact 6 have positive impact on perceived ease of use, with the path coefficient 0.23 and 0.21 each.⑦Completeness, timeliness, authority, effectiveness have positive impact on the quality of information, with the path coefficient 0.26,0.26,0.38 and 0.25 each.⑧system fact 1, flexility, privacy and self-adaptation have positive impact on the service quality. with the path coefficient 0.10,0.15,0.10, and 0.52 each.⑨Perceived efficiency, perceived performance, selectivity, perceived ease of use, perceived emotion, and self-adaptability have positive impact on the system quality, with the path coefficient 0.21,0.30,0.29,0.08,0.32 and 0.23 each.⑩System quality and information quality have positive impact on satisfaction, with the path coefficient 0.11,0.15 and 0.50 each. Finally, the SEM shows that information quality, system quality and satisfaction have positive impact on intention to use with the path coefficient 0.35, 0.55 and 0.11 each, while the service quality has negative impact on intention to use with the path coefficient-0.16...