Inference Based Query Expansion Using User’s Real Time Implicit Feedback

  • Sanasam Ranbir Singh
  • Hema A. Murthy
  • Timothy A. Gonsalves
Part of the Communications in Computer and Information Science book series (CCIS, volume 272)


Query expansion is a commonly used technique to address the problem of short and under-specified search queries in information retrieval. Traditional query expansion frameworks return static results, whereas user’s information needs is dynamics in nature. User’s search goal, even for the same query, may be different at different instances. This often leads to poor coherence between traditional query expansion and user’s search goal resulting poor retrieval performance. In this study, we observe that user’s search pattern is influenced by his/her recent searches in many search instances. We further propose a query expansion framework which explores user’s real time implicit feedback provided at the time of search to determine user’s search context and identify relevant query expansion terms. From extensive experiments, it is evident that the proposed query expansion framework adapts to the changing needs of user’s information need.


Cosine Similarity Query Term Query Expansion Expansion Term Implicit Feedback 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Sanasam Ranbir Singh
    • 1
  • Hema A. Murthy
    • 2
  • Timothy A. Gonsalves
    • 3
  1. 1.Department of Computer Science and EngineeringIndian Institute of Technology, GuwahatiGuwahatiIndia
  2. 2.Department of Computer Science and EngineeringIndian Institute of Technology, MadrasGuwahatiIndia
  3. 3.School of Computing and Electrical EngineeringIndian Institute of Technology, MandiGuwahatiIndia

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