- Minerva Elements Records
Minerva Elements Records
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ItemLoad balancing for term-distributed parallel retrievalMoffat, A ; Webber, W ; Zobel, J (ACM, 2006-01-01)
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ItemPrecision-at-ten considered redundantWebber, W ; Moffat, A ; Zobel, J ; Sakai, T (ACM, 2008-12-15)
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ItemImprovements that don't add up: Ad-hoc retrieval results since 1998Armstrong, TG ; Moffat, A ; Webber, W ; Zobel, J (ACM, 2009-12-01)
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ItemInverted files for text search enginesZobel, J ; Moffat, A (ASSOC COMPUTING MACHINERY, 2006)
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ItemA pipelined architecture for distributed text query evaluationMoffat, A ; Webber, W ; Zobel, J ; Baeza-Yates, R (SPRINGER, 2007-06)
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ItemA Similarity Measure for Indefinite RankingsWebber, W ; Moffat, A ; Zobel, J (ASSOC COMPUTING MACHINERY, 2010-11)Ranked lists are encountered in research and daily life and it is often of interest to compare these lists even when they are incomplete or have only some members in common. An example is document rankings returned for the same query by different search engines. A measure of the similarity between incomplete rankings should handle nonconjointness, weight high ranks more heavily than low, and be monotonic with increasing depth of evaluation; but no measure satisfying all these criteria currently exists. In this article, we propose a new measure having these qualities, namely rank-biased overlap (RBO). The RBO measure is based on a simple probabilistic user model. It provides monotonicity by calculating, at a given depth of evaluation, a base score that is non-decreasing with additional evaluation, and a maximum score that is nonincreasing. An extrapolated score can be calculated between these bounds if a point estimate is required. RBO has a parameter which determines the strength of the weighting to top ranks. We extend RBO to handle tied ranks and rankings of different lengths. Finally, we give examples of the use of the measure in comparing the results produced by public search engines and in assessing retrieval systems in the laboratory.
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ItemRank-Biased Precision for Measurement of Retrieval EffectivenessMoffat, A ; Zobel, J (ASSOC COMPUTING MACHINERY, 2009)A range of methods for measuring the effectiveness of information retrieval systems has been proposed. These are typically intended to provide a quantitative single-value summary of a document ranking relative to a query. However, many of these measures have failings. For example, recall is not well founded as a measure of satisfaction, since the user of an actual system cannot judge recall. Average precision is derived from recall, and suffers from the same problem. In addition, average precision lacks key stability properties that are needed for robust experiments. In this article, we introduce a new effectiveness metric, rank-biased precision , that avoids these problems. Rank-biased pre-cision is derived from a simple model of user behavior, is robust if answer rankings are extended to greater depths, and allows accurate quantification of experimental uncertainty, even when only partial relevance judgments are available.
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ItemEfficient online index construction for text databasesLester, N ; Moffat, A ; Zobel, J (ASSOC COMPUTING MACHINERY, 2008-08)Inverted index structures are a core element of current text retrieval systems. They can be constructed quickly using offline approaches, in which one or more passes are made over a static set of input data, and, at the completion of the process, an index is available for querying. However, there are search environments in which even a small delay in timeliness cannot be tolerated, and the index must always be queryable and up to date. Here we describe and analyze a geometric partitioning mechanism for online index construction that provides a range of tradeoffs between costs, and can be adapted to different balances of insertion and querying operations. Detailed experimental results are provided that show the extent of these tradeoffs, and that these new methods can yield substantial savings in online indexing costs.