Search (4 results, page 1 of 1)

  • × author_ss:"Turtle, H."
  1. Turtle, H.; Penniman, W.D.; Hickey, T.: Data entry / display devices for interactive information retrieval (1981) 0.00
    0.0023678814 = product of:
      0.0047357627 = sum of:
        0.0047357627 = product of:
          0.009471525 = sum of:
            0.009471525 = weight(_text_:a in 285) [ClassicSimilarity], result of:
              0.009471525 = score(doc=285,freq=2.0), product of:
                0.053105544 = queryWeight, product of:
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046056706 = queryNorm
                0.17835285 = fieldWeight in 285, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.109375 = fieldNorm(doc=285)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Type
    a
  2. Liu, X.; Turtle, H.: Real-time user interest modeling for real-time ranking (2013) 0.00
    0.002269176 = product of:
      0.004538352 = sum of:
        0.004538352 = product of:
          0.009076704 = sum of:
            0.009076704 = weight(_text_:a in 1035) [ClassicSimilarity], result of:
              0.009076704 = score(doc=1035,freq=10.0), product of:
                0.053105544 = queryWeight, product of:
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046056706 = queryNorm
                0.1709182 = fieldWeight in 1035, product of:
                  3.1622777 = tf(freq=10.0), with freq of:
                    10.0 = termFreq=10.0
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046875 = fieldNorm(doc=1035)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    User interest as a very dynamic information need is often ignored in most existing information retrieval systems. In this research, we present the results of experiments designed to evaluate the performance of a real-time interest model (RIM) that attempts to identify the dynamic and changing query level interests regarding social media outputs. Unlike most existing ranking methods, our ranking approach targets calculation of the probability that user interest in the content of the document is subject to very dynamic user interest change. We describe 2 formulations of the model (real-time interest vector space and real-time interest language model) stemming from classical relevance ranking methods and develop a novel methodology for evaluating the performance of RIM using Amazon Mechanical Turk to collect (interest-based) relevance judgments on a daily basis. Our results show that the model usually, although not always, performs better than baseline results obtained from commercial web search engines. We identify factors that affect RIM performance and outline plans for future research.
    Type
    a
  3. Turtle, H.; Flood, J.: Query evaluation : strategies and optimizations (1995) 0.00
    0.001913537 = product of:
      0.003827074 = sum of:
        0.003827074 = product of:
          0.007654148 = sum of:
            0.007654148 = weight(_text_:a in 4087) [ClassicSimilarity], result of:
              0.007654148 = score(doc=4087,freq=4.0), product of:
                0.053105544 = queryWeight, product of:
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046056706 = queryNorm
                0.14413087 = fieldWeight in 4087, product of:
                  2.0 = tf(freq=4.0), with freq of:
                    4.0 = termFreq=4.0
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.0625 = fieldNorm(doc=4087)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    Discusses the 2 major query evaluation strategies used in large text retrieval systems and analyzes the performance of these strategies. Discusses several optimization techniques that can be used to reduce evaluation costs and present simulation results to compare the performance of these optimization techniques when evaluating natural language queries with a collection of full text legal materials
    Type
    a
  4. Turtle, H.; Croft, W.B.: Inference networks for document retrieval (1990) 0.00
    0.0016913437 = product of:
      0.0033826875 = sum of:
        0.0033826875 = product of:
          0.006765375 = sum of:
            0.006765375 = weight(_text_:a in 1936) [ClassicSimilarity], result of:
              0.006765375 = score(doc=1936,freq=2.0), product of:
                0.053105544 = queryWeight, product of:
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046056706 = queryNorm
                0.12739488 = fieldWeight in 1936, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.078125 = fieldNorm(doc=1936)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Type
    a