Hybrid User Model for Capturing a User’s Information Seeking Intent

  • Hien Nguyen
  • Eugene SantosJr.
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 24)


A user is an important factor that contributes to the success or failure of any information retrieval system. Unfortunately, users often do not have the same technical and/or domain knowledge as the designers of such a system, while the designers are often limited in their understanding of a target user’s needs. In this chapter, we study the problem of employing a cognitive user model for information retrieval in which knowledge about a user is captured and used for improving his/her performance in an information seeking task. Our solution is to improve the effectiveness of a user in a search by developing a hybrid user model to capture user intent dynamically and combines the captured intent with an awareness of the components of an information retrieval system. The term “hybrid” refers to the methodology of combining the understanding of a user with the insights into a system all unified within a decision theoretic framework. In this model, multi-attribute utility theory is used to evaluate values of the attributes describing a user’s intent in combination with the attributes describing an information retrieval system. We use the existing research on predicting query performance and on determining dissemination thresholds to create functions to evaluate these selected attributes. This approach also offers fine-grained representation of the model and the ability to learn a user’s knowledge dynamically. We compare this approach with the best traditional approach for relevance feedback in the information retrieval community—Ide dec-hi, using term frequency inverted document frequency (TFIDF) weighting on selected collections from the information retrieval community such as CRANFIELD, MEDLINE, and CACM. The evaluations with our hybrid model with these testbeds show that this approach retrieves more relevant documents in the first 15 returned documents than the TFIDF approach for all three collections, as well as more relevant documents on MEDLINE and CRANFIELD in both initial and feedback runs, while being competitive with the Ide dec-hi approach in the feedback runs for the CACM collection. We also demonstrate the use of our user model to dynamically create a common knowledge base from the users’ queries and relevant snippets using the APEX 07 data set.


Hybrid user model Information retrieval Relevance feedback User intent Decision theory Context 


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Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  1. 1.Department of Mathematical and Computer SciencesUniversity of Wisconsin-WhitewaterWhitewaterUSA
  2. 2.Dartmouth College Thayer School of EngineeringHanoverUSA

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