Predicting Re-finding Activity and Difficulty

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• 作者：Sargol Sadeghi (19)
  Roi Blanco (20)
  Peter Mika (20)
  Mark Sanderson (19)
  Falk Scholer (19)
  David Vallet (21)
  
  19. RMIT University ; Melbourne ; Australia
  20. Yahoo! Research ; Barcelona ; Spain
  21. Google ; Sydney ; Australia
  
• 关键词：Re ; finding Identification ; Difficulty Detection ; Behavioral Features
• 刊名：Lecture Notes in Computer Science
• 出版年：2015
• 出版时间：2015
• 年：2015
• 卷：9022
• 期：1
• 页码：715-727
• 全文大小：217 KB
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  18. Teevan, J., Adar, E., Jones, R., Potts, M.A.: Information re-retrieval: Repeat queries in yahoo鈥檚 logs. In: Proc. SIGIR, pp. 151鈥?58. ACM (2007)
  19. Tyler, S.K., Teevan, J.: Large scale query log analysis of re-finding. In: Proc. WSDM, pp. 191鈥?00. ACM (2010)
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• 作者单位：Advances in Information Retrieval
• 丛书名：978-3-319-16353-6
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

In this study, we address the problem of identifying if users are attempting to re-find information and estimating the level of difficulty of the re-finding task. We propose to consider the task information (e.g. multiple queries and click information) rather than only queries. Our resultant prediction models are shown to be significantly more accurate (by 2%) than the current state of the art. While past research assumes that previous search history of the user is available to the prediction model, we examine if re-finding detection is possible without access to this information. Our evaluation indicates that such detection is possible, but more challenging. We further describe the first predictive model in detecting re-finding difficulty, showing it to be significantly better than existing approaches for detecting general search difficulty.