RELEVANCE: A review of and a framework for the thinking on the notion in information science

By Tefko Saracevic. 1997

Subfields: Information Retrieval & Search

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In Information Retrieval & Search

1. Jimmy J. Lin
2. Andrew Yates
3. Rodrigo Nogueira
4. Pia Borlund
5. Jun Xu
6. Yiyu Yao
7. William R. Hersh
8. Hang Li

Citations in the corpus

• Pretrained Transformers for Text Ranking: BERT and Beyond (2020, background): “Saracevic [2017] offered a relatively recent survey after having studied the problem for at least half a century [Saracevic, 1975].”
• A review of Web searching studies and a framework for future research (2001, background): “Relevance to a searcher is not clearly defined (Mizzaro, 1997; Saracevic, 1975; Spink, Greisdorf, & Bateman, 1998).”
• Information Retrieval: A Biomedical and Health Perspective (2020, background): “• Genomics Track in 2006 and 2007 [20].”
• The Notion of Relevance in Information Science: Everybody knows what relevance is. But, what is it really? (2016, background): “From the first relevance experiments that began in 1961 to 1975 some 15 studies reported on experiments that directly involved relevance, as summarized in Saracevic (1975). For whatever reason from 1975 till 1988 there was a hiatus in relevance experimentation—only one experiment was reported in tha”
• Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology (1989, background): “The fact that relevance and usefulness load on the same factor agrees with information scientists, who emphasize the conceptual similarity be-tween the usefulness and relevance notions (Saracevic, 1975).”
• A review of content-based image retrieval systems in medical applications - clinical benefits and future directions (2004, background): “The subject of relevance alone can fill several books [176,177].”
• Measuring Retrieval Effectiveness Based on User Preference of Documents (1995, methodology): “Typically, user judgments are described using the concept of relevance (Bookstein, 1979; Cooper, 1971; Cuadra & Katter, 1967; Saracevic, 1970, 1975).”
• Methods for Evaluating Interactive Information Retrieval Systems with Users (2009, background): “, [33, 197, 224, 232, 235, 236]); (2) the criteria users employ when making relevance assessments (e.”
• Measuring search engine bias (2005, background): “Like traditional measures of retrieval performance (i.e., recall and precision), bias is a function of a system’s response to a query, but it does not depend on a determination of relevance (Meadow, 1973; Saracevic, 1975; Wishard, 1998).”
• Semantic Matching in Search (2014, background): “Among them, relevance [156, 171, 157] is the most important factor, which is also the focus of this survey.”
• Relevance: A review of the literature and a framework for thinking on the notion in information science. Part II: nature and manifestations of relevance (2007, background): “(Saracevic, 1975, p. 328) Almost every definition offered still fits this pattern.”
• Experimental components for the evaluation of interactive information retrieval systems (2000, methodology): “The analytical concept of the information need formation is supported by several empirical works, such as the protocol analyses performed by Belkin [32] and Ingwersen [33], as well as Saracevic, Mokros and Su's [34] study on the nature of online search interaction between users and intermediaries.”