Search (1 results, page 1 of 1)

  • × author_ss:"Allan, J."
  • × theme_ss:"Retrievalalgorithmen"
  • × year_i:[2020 TO 2030}
  1. Dang, E.K.F.; Luk, R.W.P.; Allan, J.: ¬A retrieval model family based on the probability ranking principle for ad hoc retrieval (2022) 0.00
    3.1188648E-4 = product of:
      0.004678297 = sum of:
        0.004678297 = weight(_text_:in in 638) [ClassicSimilarity], result of:
          0.004678297 = score(doc=638,freq=2.0), product of:
            0.044469737 = queryWeight, product of:
              1.3602545 = idf(docFreq=30841, maxDocs=44218)
              0.032692216 = queryNorm
            0.10520181 = fieldWeight in 638, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.3602545 = idf(docFreq=30841, maxDocs=44218)
              0.0546875 = fieldNorm(doc=638)
      0.06666667 = coord(1/15)
    
    Abstract
    Many successful retrieval models are derived based on or conform to the probability ranking principle (PRP). We present a new derivation of a document ranking function given by the probability of relevance of a document, conforming to the PRP. Our formulation yields a family of retrieval models, called probabilistic binary relevance (PBR) models, with various instantiations obtained by different probability estimations. By extensive experiments on a range of TREC collections, improvement of the PBR models over some established baselines with statistical significance is observed, especially in the large Clueweb09 Cat-B collection.