Okapi at TREC4,Stephen E. Robertson,Steve Walker,Micheline Hancock-beaulieu,Mike Gatford,A. Payne

Okapi at TREC4   (Citations: 405)
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The Okapi software used for TREC-3 was similar to that used in previous TRECs, comprising a low level basic search system and a user interface for the manual search experiments, together with data conversion and inversion utilities. There were also various scripts and programs for generating query terms, running batches of trials and performing evaluation. The main code is written in C, with additional material in awk and perl. The evaluation program is from Chris Buckley at Cornell.
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    • ...Finally, CF -based recommendation is performed with a novel ranking model which extends the Best Match (BM) model [7]‐[9] to rank the candidate TV program contents for recommendation...
    • ...The details of finding a right value are described in Section IV. And as a rank model for ordered recommendation of TV program contents, we propose a novel rank model based on the BM model [7]‐[9]...
    • ...Our proposed ranked model extends the Best Match (BM) model [7]‐[9]...
    • ...But, the second condition is not always satisfied as . To remedy this, a scaling factor is added in the numerator, thus resulting in . This is taken into account in the BM11, 15 and 25 models [7]...
    • ...The BM25 includes an inappropriate condition for TV program recommendation since it gives a high weight on short documents compared to long documents by scope hypothesis [7]...
    • ...The Robertson et al. analyzed the way of weighting in details [7]...
    • ...This weight puts more emphasis on the active user’s personal preference on TV program contents, which is not reflected in the original BM [7], [8]...

    Eunhui Kimet al. An Automatic Recommendation Scheme of TV Program Contents for (IP)TV P...

    • ...Many rankers are thus derived with the different assumptions of query model and document model such as Okapi [12], Kullback-Leibler divergence and query log-likelihood [6, 7, 8], cosine distance with the tfidf features [2]...

    Sheng Gaoet al. Effective Large Scale Text Retrieval via Learning Risk-Minimization an...

    • ...Many retrieval models have been proposed and studied including vector space models [19], classic probabilistic models [18,23,7], language models [15,25] and recently proposed axiomatic models [5]...
    • ...To evaluate the effectiveness of the proposed methods, we integrate them into four representative retrieval functions (i.e., pivoted normalization retrieval function [21], Okapi BM25 retrieval function [18], Dirichlet prior retrieval function [25] and axiomatic retrieval function [5]), and conduct experiments over eight representative TREC data sets...
    • ...The functions are pivoted normalization function derived from vector space models [19,21]), Okapi BM25 derived from classical probabilistic models [23,7,18], Dirichlet prior derived from language models [15,25] and F2-EXP derived from axiomatic retrieval models [5]...

    Wei Zhenget al. Query Aspect Based Term Weighting Regularization in Information Retrie...

    • ...PRF has proven to be an effective technique for improving IR performance [2,3,4,5,6,7]...

    Zheng Yeet al. Exploring Social Annotation Tags to Enhance Information Retrieval Perf...

    • ...Okapi BM25 has been used to estimate document relevance given a query: its parameters have been set to standard values (Robertson et al, 1995)...

    Teerapong Leelanupabet al. Revisiting Subtopic Retrieval in the ImageCLEF 2009 Photo Retrieval Ta...

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