A re-examination of relevance: toward a dynamic, situational definition∗

Schamber, Linda; Eisenberg, Michael B.; Nilan, Michael S.

doi:10.1016/0306-4573(90)90050-c

Cited by 362 publications

(279 citation statements)

References 31 publications

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“…Table 1 presents in the first row the percentage of relevant documents which contain at least one query term for each of the six collections 2 used in this work, and in the second row the average number of query terms contained in a relevant document. The figures in the first row of this table all exceed 91%, an exceptionally high value that verifies the highly topical and algorithmic nature of relevance that is employed in standard IR evaluation (Schamber et al, 1990). The implication of this for the query-sensitive measures presented here, and especially for M1, is that the likelihood for pairs of co-relevant documents to contain at least one query term in common is high.…”

Section: Query-sensitive Similarity Measuresmentioning

confidence: 56%

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Query-Sensitive Similarity Measures for Information Retrieval

Tombros

Rijsbergen

2004

Knowledge and Information Systems

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Abstract. The application of document clustering to information retrieval has been motivated by the potential effectiveness gains postulated by the cluster hypothesis. The hypothesis states that relevant documents tend to be highly similar to each other, and therefore tend to appear in the same clusters. In this paper we propose an axiomatic view of the hypothesis, by suggesting that documents relevant to the same query (co-relevant documents) display an inherent similarity to each other which is dictated by the query itself. Because of this inherent similarity, the cluster hypothesis should be valid for any document collection. Our research describes an attempt to devise means by which this similarity can be detected. We propose the use of query-sensitive similarity measures that bias interdocument relationships towards pairs of documents that jointly possess attributes that are expressed in a query. We experimentally tested three query-sensitive measures against conventional ones that do not take the context of the query into account, and we also examined the comparative effectiveness of the three query-sensitive measures. We calculated interdocument relationships for varying numbers of top-ranked documents for six document collections. Our results show a consistent and significant increase in the number of relevant documents that become nearest neighbours of any given relevant document when query-sensitive measures are used. These results suggest that the effectiveness of a cluster-based IR system has the potential to increase through the use of query-sensitive similarity measures.

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Section: Query-sensitive Similarity Measuresmentioning

confidence: 56%

“…Therefore, implicitly the notion of topicality (Saracevic, 1970) is adopted for relevance. It is well established in IR research that relevance is a multidimensional concept, and that topicality is only one such aspect (Schamber et al, 1990).…”

Section: Limitationsmentioning

confidence: 99%

Query-Sensitive Similarity Measures for Information Retrieval

Tombros

Rijsbergen

2004

Knowledge and Information Systems

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“…local news concerning local issues). These findings mirrored previous studies that have highlighted the dynamic and multidimensional nature of relevance, where many factors beyond topicality and aboutness influence how a user assesses information [1,2,3].…”

Section: The Peng Systemsupporting

confidence: 85%

The PENG System: Integrating Push and Pull for Information Access

Baillie

Bordogna

Crestani

et al.

Asian Digital Libraries. Looking Back 10 Years and Forging New Frontiers

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Abstract. This paper describes the PENG project that integrates personalized push and pull technologies to access relevant information. PENG integrates several key tasks, including personalized filtering, retrieval, and presentation of multimedia news, into a single system. In this paper we provide an overview of PENG, describing our approach to constructing a dedicated retrieval and content management system for a specific user group. We also report critically on the results of a user and task based evaluation.

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“…Student reactions to what they find are classically considered under the relevance of information retrieval research, although relevance is a multi-dimensional construct (Schamber et al, 1990) and -for students, at least, gets mixed up with convenience of access to full text (Connaway et al, 2011, Urquhart et al, 2003b). Steinerová's (2008) conclusion that relevance is linked to 'value', 'utility', and 'importance' may be diluted by the convenience of availability, and pertinence a more accurate description than relevance.…”

Section: Literature Reviewmentioning

confidence: 99%

Profiling information behaviour of nursing students: part 2: derivation of profiles

Stokes

Urquhart

2015

Journal of Documentation

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PurposeThe aim is to develop information behaviour profiles for nursing students, to help inform information literacy programmes. Methodology The methods include further analysis of quantitative findings (previously reported in part 1), together with qualitative research data collection and analysis. Critical incident type interviews with 11 students were transcribed and analysed using an interpretative categorisation method that used dendrograms for data display and analysis. From the regression analysis of the quantitative data, the micro-processes for information seeking were linked to learning styles, and then to personality traits to generate information seeking profiles. Integration of the qualitative findings led to development of a task-based information search model. Findings The start list of seven categories for qualitative analysis (derived from a literature review) was refined (one category added, one removed, with some relabeling). The quantitative data analysis revealed seven profiles (Deep Adventurer, Deep Identifier, Deep Investigator, Strategic All-rounder, Strategic Collector, Surface Co-ordinator, Surface Skimmer, each linked to a particular learning style, personality trait, and preferred information seeking microprocesses). Research implications/limitationsThe data was collected at only one university and the profiles and the model need to be validated with data from other groups of nursing students. The findings on micro-processes consolidate and extend previous research. Practical implications The profiles should inform information literacy programmes as they show that information search profiles may be more varied than assumed. The information search model extends previous task-based information search models. Originality The information search profiles have not been identified previously. IntroductionPart I of the paper (Stokes and Urquhart, 2011) discussed the quantitative findings of a mixed-methods doctoral research study on the development of information seeking profiles among nursing students, based on personality, self-efficacy and learning style. The quantitative element of the study used a questionnaire (sample n=194) consisting of three validated scales for personality (Saucier, 1994), learning styles (Entwistle, 1997), and self-efficacy with information literacy (Kurbanoglu et al., 2006), a section on information seeking preferences based on Foster's (2004) model, as well as some demographic questions.Bawden and Robinson's (2011) chapter on 'information styles' summarises many studies that investigated the role of learning styles, self-efficacy, and personality in terms of information-related behaviour finding many plausible and consistent relationships. Particular personality traits and differing learning styles seem to influence the way individuals search for information with individual studies finding marked relationships between Conscientiousness and Strategic learners; and between Openness and Deep learners (Diseth, 2003, Diseth and Martinsen, 2003, Diseth, 2011. These re...

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A re-examination of relevance: toward a dynamic, situational definition∗

Cited by 362 publications

References 31 publications

Query-Sensitive Similarity Measures for Information Retrieval

Query-Sensitive Similarity Measures for Information Retrieval

The PENG System: Integrating Push and Pull for Information Access

Profiling information behaviour of nursing students: part 2: derivation of profiles

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