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  • × author_ss:"Turtle, H."
  1. Turtle, H.; Croft, W.B.: Inference networks for document retrieval (1990) 0.00 
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    Footnote
    Wiederabgedruckt in: Readings in information retrieval. Ed.: K. Sparck Jones u. P. Willett. San Francisco: Morgan Kaufmann 1997. S.287-298
    Source
    Proceedings of the thirteenth international conference on research and development in information retrieval
  2. Turtle, H.; Penniman, W.D.; Hickey, T.: Data entry / display devices for interactive information retrieval (1981) 0.00 
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  3. Turtle, H.; Flood, J.: Query evaluation : strategies and optimizations (1995) 0.00 
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    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
  4. Liu, X.; Turtle, H.: Real-time user interest modeling for real-time ranking (2013) 0.00 
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    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.

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