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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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Gross et al. (2015) have demonstrated that about a quarter of hits would typically be lost to keyword searchers if contemporary academic library catalogs dropped their controlled subject headings. This article re- ports on an investigation of the search value that subject descriptors and identifiers assigned by professional indexers add to a bibliographic database, namely the Australian Education Index (AEI). First, a similar methodology to that developed by Gross et al. (2015) was applied, with keyword searches representing a range of educational topics run on the AEI database with and without its subject indexing. The results indicated that AEI users would also lose, on average, about a quarter of hits per query. Second, an alternative research design was applied in which an experienced literature searcher was asked to find resources on a set of educational topics on an AEI database stripped of its subject indexing and then asked to search for additional resources on the same topics after the subject indexing had been reinserted. In this study, the proportion of additional resources that would have been lost had it not been for the subject indexing was again found to be about a quarter of the total resources found for each topic, on average.
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Faceted browsing is a common feature of new library catalog interfaces. But to what extent does it improve user performance in searching within today’s library catalog systems? This article reviews the literature for user studies involving faceted browsing and user studies of “next-generation” library catalogs that incorporate faceted browsing. Both the results and the methods of these studies are analyzed by asking, What do we currently know about faceted browsing? How can we design better studies of faceted browsing in library catalogs? The article proposes methodological considerations for practicing librarians and provides examples of goals, tasks, and measurements for user studies of faceted browsing in library catalogs.
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Topic
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Information retrieval
- Faceted search (7)
- Implicit feedback (3)
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Ranking
(2)
- Diversity (1)
- Relevance (4)
- Search log analysis (4)
- Information behavior (3)
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Knowledge organization
(4)
- Facet analysis (2)
- Ontology (1)
Field of study
- Computer science (7)
- Information science (16)
Contribution
- Algorithm (1)
- Conceptual model (7)
- Empirical study (9)
- Evaluation model (3)
- Literature review (3)
- Methodology (2)
- Primer (1)