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In 1975 Tefko Saracevic declared “the subject knowledge view” to be the most fundamental perspective of relevance. This paper examines the assumptions in different views of relevance, including “the system's view” and “the user's view” and offers a reinterpretation of these views. The paper finds that what was regarded as the most fundamental view by Saracevic in 1975 has not since been considered (with very few exceptions). Other views, which are based on less fruitful assumptions, have dominated the discourse on relevance in information retrieval and information science. Many authors have reexamined the concept of relevance in information science, but have neglected the subject knowledge view, hence basic theoretical assumptions seem not to have been properly addressed. It is as urgent now as it was in 1975 seriously to consider “the subject knowledge view” of relevance (which may also be termed “the epistemological view”). The concept of relevance, like other basic concepts, is influenced by overall approaches to information science, such as the cognitive view and the domain-analytic view. There is today a trend toward a social paradigm for information science. This paper offers an understanding of relevance from such a social point of view.
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The Probabilistic Relevance Framework (PRF) is a formal framework for document retrieval, grounded in work done in the 1970–1980s, which led to the development of one of the most successful text-retrieval algorithms, BM25. In recent years, research in the PRF has yielded new retrieval models capable of taking into account document meta-data (especially structure and link-graph information). Again, this has led to one of the most successful Web-search and corporate-search algorithms, BM25F. This work presents the PRF from a conceptual point of view, describing the probabilistic modelling assumptions behind the framework and the different ranking algorithms that result from its application: the binary independence model, relevance feedback models, BM25 and BM25F. It also discusses the relation between the PRF and other statistical models for IR, and covers some related topics, such as the use of non-textual features, and parameter optimisation for models with free parameters.
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The goal of the Redundancy, Diversity, and Interdependent Document Relevance workshop was to explore how ranking, performance assessment and learning to rank can move beyond the assumption that the relevance of a document is independent of other documents. In particular, the workshop focussed on three themes: the effect of redundancy on information retrieval utility (for example, minimizing the wasted effort of users who must skip redundant information), the role of diversity (for example, for mitigating the risk of misinterpreting ambiguous queries), and algorithms for set-level optimization (where the quality of a set of retrieved documents is not simply the sum of its parts). This workshop built directly upon the Beyond Binary Relevance: Preferences, Diversity and Set-Level Judgments workshop at SIGIR 2008 [3], shifting focus to address the questions left open by the discussions and results from that workshop. As such, it was the first workshop to explicitly focus on the related research challenges of redundancy, diversity, and interdependent relevance – all of which require novel performance measures, learning methods, and evaluation techniques. The workshop program committee consisted of 15 researchers from academia and industry, with experience in IR evaluation, machine learning, and IR algorithmic design. Over 40 people attended the workshop. This report aims to summarize the workshop, and also to systematize common themes and key concepts so as to encourage research in the three workshop themes. It contains our attempt to summarize and organize the topics that came up in presentations as well as in discussions, pulling out common elements. Many audience members contributed, yet due to the free-flowing discussion, attributing all the observations to particular audience members is unfortunately impossible. Not all audience members would necessarily agree with the views presented, but we do attempt to present a consensus view as far as possible.
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All is flux. —Plato on Knowledge in the Theaetetus (about 369 BC) Relevance is a, if not even the, key notion in information science in general and information retrieval in particular. This two-part critical review traces and synthesizes the scholarship on relevance over the past 30 years or so and provides an updated framework within which the still widely dissonant ideas and works about relevance might be interpreted and related. It is a continuation and update of a similar review that appeared in 1975 under the same title, considered here as being Part I. The present review is organized in two parts: Part II addresses the questions related to nature and manifestations of relevance, and Part III addresses questions related to relevance behavior and effects. In Part II, the nature of relevance is discussed in terms of meaning ascribed to relevance, theories used or proposed, and models that have been developed. The manifestations of relevance are classified as to several kinds of relevance that form an interdependent system of relevancies. In Part III, relevance behavior and effects are synthesized using experimental and observational works that incorporated data. In both parts, each section concludes with a summary that in effect provides an interpretation and synthesis of contemporary thinking on the topic treated or suggests hypotheses for future research. Analyses of some of the major trends that shape relevance work are offered in conclusions.
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