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

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

Abstract

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.

Keywords

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

References

  1. 1.
    Allen, R.: User models: theory, method and practice. Int. J. Man Mach. Stud. 32, 511–543 (1990)CrossRefGoogle Scholar
  2. 2.
    Baeza-Yates, R., Calderón-Benavides, L., Gonzalez-Caro, C.: The intention behind Web queries. In: Proceedings of String Processing and Information Retrieval 2006, pp. 98–109. Glasgow, Scotland (2006)Google Scholar
  3. 3.
    Baeza-Yates, R., Raghavan, P.: Next generation Web search. In: Ceri, S., Brambilla, M. (eds.) Search Computing. Lecture Notes in Computer Science, vol. 5950, pp. 11–23. Springer, Berlin (2010)Google Scholar
  4. 4.
    Baeza-Yates, R., Ribiero-Neto, B.: Modern Information Retrieval. Addison-Wesley, New York (1999)Google Scholar
  5. 5.
    Balabanovic, M.: Exploring versus exploiting when learning user models for text recommendation. User Model. User-Adap. Inter. 8(1–2), 71–102 (1998)CrossRefGoogle Scholar
  6. 6.
    Balabanovic, M., Shoham, Y.: Content-based collaborative recommendation. Commun. ACM 40(3), 66–72 (1997)CrossRefGoogle Scholar
  7. 7.
    Belkin. N.J.: Interaction with text: information retrieval as information seeking behavior. Information retrieval. 10. von der Modelierung zur Anwerdung, pp. 55–66. Universitaetsverlag, Konstanz (1993)Google Scholar
  8. 8.
    Belkin, N.J., Oddy, R.N., Brooks, H.M.: Ask for information retrieval: part I: background and theory. J. Doc. 38(2), 61–71 (1982)CrossRefGoogle Scholar
  9. 9.
    Belkin, N.J. Windel, G.: Using monstrat for the analysis of information interaction. In: IRFIS 5, Fifth International Research Forum in Information Science, pp. 359–382. Heidelberg (1984)Google Scholar
  10. 10.
    Billsus, D., Pazzani, M.J.: User modeling for adaptive news access. User Model. User-Adap. Inter. 10(2–3), 147–180 (2000)CrossRefGoogle Scholar
  11. 11.
    Bodoff, D., Raban, D.: User models as revealed in web-based research services. J. Am. Soc. Inform. Sci. Technol. 63(3), 584–599 (2012)CrossRefGoogle Scholar
  12. 12.
    Borlund, P.: The concept of relevance in information retrieval. J. Am. Soc. Inform. Sci. Technol. 54(10), 913–925 (2003)CrossRefGoogle Scholar
  13. 13.
    Boughanem, M., Tmar, M.: Incremental adaptive filtering: profile learning and threshold calibration. In: Proceedings of SAC 2002, pp. 640–644. Madrid, Spain (2002)Google Scholar
  14. 14.
    Brajnik, G., Guida, G., Tasso, C.: User modeling in intelligent information retrieval. Inf. Process. Manage. 23(4), 305–320 (1987)CrossRefGoogle Scholar
  15. 15.
    Broder, A.: A taxonomy of Web search. SIGIR Forum 36(2), 3–10 (2002)CrossRefGoogle Scholar
  16. 16.
    Brown, S.M.: Decision theoretic approach for interface agent development. Ph.D. thesis, Air Force Institute of Technology (1998)Google Scholar
  17. 17.
    Campbell, I., van Rijsbergen, C.J.: Ostensive model of developing information needs. In: Proceedings of the Second International Conference on Conceptions of Library and Information Science: Integration in Perspective (CoLIS 2), pp. 251–268 (1996)Google Scholar
  18. 18.
    Cecchini, R.L., Lorenzetti, C.M., Maguitman, A.G., Brignole, N.B.: Using genetic algorithms to evolve a population of topical queries. Inf. Process. Manage. 44(6), 1863–1878 (2008)CrossRefGoogle Scholar
  19. 19.
    Chen, S.Y., Magoulas, G.D., Dimakopoulos, D.: A flexible interface design for Web directories to accommodate different cognitive styles. J. Am. Soc. Inform. Sci. Technol. 56(1), 70–83 (2005)CrossRefGoogle Scholar
  20. 20.
    Cooper, W., Maron, M.E.: Foundations of probabilistic and utility theoretic indexing. J. Assoc. Comput. Mach. 25(1), 67–80 (1978)MathSciNetMATHCrossRefGoogle Scholar
  21. 21.
    Donghee, Y.: Hybrid query processing for personalized information retrieval on the Semantic Web. Knowl.-Based Syst. 27, 211–218 (2012)Google Scholar
  22. 22.
    Drucker, H., Shahrary, B., Gibbon, C.: Support vector machines: relevance feedback and information retrieval. Inf. Process. Manage. 38(3), 305–323 (2002)MATHCrossRefGoogle Scholar
  23. 23.
    Ducheneaut, N., Partidge, K., Huang, Q., Price, B., Roberts, M., Chi, E.H., Belotti, V., Begole, B.: Collaborative filtering is not enough? Experiments with a mixed-model recommender for leisure activities. In: Proceeding of the Seventeenth International Conference, User Modeling, Adaptation, and Personalization, pp. 295–306. Trento, Italy (2009)Google Scholar
  24. 24.
    Duong H.T., Uddin, M.N., Lim D., Jo, G.: A collaborative ontology-based user profiles system. In: Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems, pp. 540–552 (2009)Google Scholar
  25. 25.
    Efthimis, E.N.: Query expansion. In: Williams, M. (ed.) Ann Rev Inf Sci Technol 31, 121–187 (1996)Google Scholar
  26. 26.
    Frake, W.B., Baeza-Yates, R.: Information retrieval: data structures and algorithms, p. 07458. Prentice Hall PTR, Upper Saddle River (1992)Google Scholar
  27. 27.
    Ghorab M.R., Zhou D., O’Connor A., Wade, V.: Personalised information retrieval: survey and classification. User Modeling and User-Adapted Interaction. Online first (2012)Google Scholar
  28. 28.
    He, B., Ounis, I.: Inferring query performance using pre-retrieval predictors’. In: Information Systems, Special Issue for the String Processing and Information Retrieval: 11th International Conference, pp. 43–54 (2004)Google Scholar
  29. 29.
    Ide, E.: New experiment in relevance feedback. In: The Smart System Experiments in Automatic Documents Processing, pp. 337–354 (1971)Google Scholar
  30. 30.
    Ingwersen, P.: Information Retrieval Interaction. Taylor Graham, London (1992)Google Scholar
  31. 31.
    Jansen, B., Booth, D., Spink, A. Determining the user intent of Web search engine queries. In: Proceedings of the International World Wide Web Conference, pp. 1149–1150. Alberta, Canada (2007)Google Scholar
  32. 32.
    Jensen, F.V.: An Introduction to Bayesian Networks. University College London Press, London (1996)Google Scholar
  33. 33.
    Keeney, L.R., Raiffa, H.: Decision with Multiple Objectives: Preferences and Value Tradeoffs. Wiley, New York (1976)Google Scholar
  34. 34.
    Kim, J.: Describing and predicting information-seeking behavior on the Web. J. Am. Soc. Inform. Sci. Technol. 60(4), 679–693 (2009)CrossRefGoogle Scholar
  35. 35.
    Kofler, C., Lux, M.: Dynamic presentation adaptation based on user intent classification. In: Proceedings of the 17th ACM International Conference on Multimedia (MM ‘09), pp. 1117–1118. ACM, New York, USA (2009)Google Scholar
  36. 36.
    Kumaran, G., Allan, J.: Adapting information retrieval systems to user queries. Inf. Process. Manage. 44(6), 1838–1862 (2008)CrossRefGoogle Scholar
  37. 37.
    Lau, T., Horvitz, E.: Patterns of search: analyzing and modeling Web query refinement. In: Proceedings of the Seventh International Conference on User Modeling, pp. 119–128. Banff, Canada (1999)Google Scholar
  38. 38.
    Lee, U., Liu, Z., Cho, J.: Automatic identification of user goals in web search. In: Proceedings of the International World Wide Web Conference 2005, pp. 391–400. Chiba, Japan (2005)Google Scholar
  39. 39.
    Logan, B., Reece, S., Sparck, J.: Modeling information retrieval agents with belief revision. In: Proceedings of the Seventeenth Annual ACM/SIGIR Conference on Research and Development in Information Retrieval, pp. 91–100 (1994)Google Scholar
  40. 40.
    Lopér-Pujalte, C., Guerrero-Bote, V., Moya-Anegon, F.D.: Genetic algorithms in relevance feedback: a second test and new contributions. Inf. Process. Manage. 39(5), 669–697 (2003)CrossRefGoogle Scholar
  41. 41.
    Lynch, C.: The next generation of public access information retrieval systems for research libraries: lessons from ten years of the MELVYL system. Inf. Technol. Libr. 11(4), 405–415 (1992)Google Scholar
  42. 42.
    Mat-Hassan, M., Levene, M.: Associating search and navigation behavior through log analysis. J. Am. Soc. Inform. Sci. Technol. 56(9), 913–934 (2005)CrossRefGoogle Scholar
  43. 43.
    Michard, M.: Graphical presentation of boolean expressions in a database query language: design notes and an ergonomic evaluation. Behav. Inf. Technol. 1(3), 279–288 (1982)CrossRefGoogle Scholar
  44. 44.
    Miller, G.A.: WordNet: a lexical database for English. Commun. ACM 38(11), 39–41 (1995)CrossRefGoogle Scholar
  45. 45.
    Nguyen, H.: Capturing user intent for information retrieval. Ph.D. Dissertation, University of Connecticut (2005)Google Scholar
  46. 46.
    Nguyen, H., Haddawy, P.: The decision-theoretic interactive video advisor. In: Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence- UAI 99, pp. 494–501. Stockholm, Sweden (1999)Google Scholar
  47. 47.
    Nguyen, H., Santos, E. Jr, Schuet, A., Smith, N.: Hybrid user model for information retrieval. In: Technical Report of Modeling Others from Observations workshop at Twenty-First National Conference on Artificial Intelligence (AAAI) conference, pp. 61–68. Boston (2006)Google Scholar
  48. 48.
    Nguyen, H., Santos, E.J., Zhao, Q., Lee, C.: Evaluation of effects on retrieval performance for an adaptive user model. In: Adaptive Hypermedia 2004 Workshop Proceedings—Part I, pp. 193–202., Eindhoven, The Netherlands (2004a)Google Scholar
  49. 49.
    Nguyen, H., Santos, E.J., Zhao, Q., Wang, H.: Capturing user intent for information retrieval. In: Proceedings of the Human Factors and Ergonomics Society 48th Annual Meeting, pp. 371–375. New Orleans, LA (2004b)Google Scholar
  50. 50.
    Pearl, J.: Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kaufmann, San Mateo (1988)Google Scholar
  51. 51.
    Rich, E.: User modeling via stereotypes. Cogn. Sci. 3, 329–354 (1979)CrossRefGoogle Scholar
  52. 52.
    Rich, E.: Users are individuals: individualizing user models. Int. J. Man Mach. Stud. 18, 199–214 (1983)CrossRefGoogle Scholar
  53. 53.
    Rochio, J.J.: Relevance feedback in information retrieval. In: The Smart system—experiments in automatic document processing, pp. 313–323 (1971)Google Scholar
  54. 54.
    Rose, D., Levinson, D.: Understanding user goals in Web search. In: Proceedings of the International World Wide Web Conference 2004, pp. 13–19. New York, USA (2004)Google Scholar
  55. 55.
    Ruthven, I., Lalmas, M.: A survey on the use of relevance feedback for information access systems. Knowl. Eng. Rev. 18(2), 95–145 (2003)CrossRefGoogle Scholar
  56. 56.
    Ruthven, I., Lalmas, M., van Rijsbergen, K.: Incorporating user search behavior into relevance feedback. J. Am. Soc. Inform. Sci. Technol. 54(6), 529–549 (2003)CrossRefGoogle Scholar
  57. 57.
    Salton, G., Buckley, C.: Improving retrieval performance by relevance feedback. J. Am. Soc. Inf. Sci. 41(4), 288–297 (1990)CrossRefGoogle Scholar
  58. 58.
    Santos, E.J., Nguyen, H.: Modeling users for adaptive information retrieval by capturing user intent. In: Chevalier, M., Julien, C., Soulé, C. (eds.) Collaborative and Social Information Retrieval and Access: Techniques for Improved User Modeling, pp. 88–118. IGI Global (2009)Google Scholar
  59. 59.
    Santos, E.J., Nguyen, H., Brown, S.M.: Kavanah: an active user interface for information retrieval application. In: Proceedings of 2nd Asia-Pacific Conference on Intelligent Agent Technology, pp. 412–423, Japan (2001)Google Scholar
  60. 60.
    Santos, E.J., Nguyen, H., Zhao, Q., Pukinskis, E.: Empirical evaluation of adaptive user modeling in a medical information retrieval application. In: Proceedings of the Ninth User Modeling Conference, pp. 292–296, Johnstown (2003a)Google Scholar
  61. 61.
    Santos, E.J., Nguyen, H., Zhao, Q., Wang, H.: User modeling for intent prediction in information analysis. In: Proceedings of the 47th Annual Meeting for the Human Factors and Ergonomics Society (HFES-03), pp. 1034–1038, Denver (2003b)Google Scholar
  62. 62.
    Saracevic, T.: Relevance reconsidered. In: Ingwersen, P., Pors, P.O. (eds.) Proceedings of the Second International Conference on Conceptions of Library and Information Science: Integration in Perspective. Copenhagen: The Royal School of Librarianship, pp. 201–218 (1996)Google Scholar
  63. 63.
    Saracevic, T., Spink A., Wu, M.: Users and intermediaries in information retrieval: what are they talking about? In: Proceedings of the Sixth International Conference in User Modeling - UM 97, pp. 43–54 (1997)Google Scholar
  64. 64.
    Sleator, D.D., Temperley D.: Parsing English with a link grammar. In: Proceedings of the Third International Workshop on Parsing Technologies, pp. 277–292 (1993)Google Scholar
  65. 65.
    Spink, A., Cole, C.: New Directions in Cognitive Information Retrieval. The Information Retrieval Series. Springer (2005)Google Scholar
  66. 66.
    Spink, A., Losee, R.M.: Feedback in information retrieval. In: Williams, M. (ed.) Ann.Rev. Inf. Sci. Technol. 31, 33–78 (1996)Google Scholar
  67. 67.
    Spink, A., Greisdorf, H., Bateman, J.: From highly relevant to not relevant: examining different regions of relevance. Inf. Process. Manage. 34(5), 599–621 (1998)CrossRefGoogle Scholar
  68. 68.
    Steichen, B., Ashman, H., Wade, V.: A comparative survey of personalised information retrieval and adaptive hypermedia techniques. Inf. Process. Manage. 48, 698–724 (2012)CrossRefGoogle Scholar
  69. 69.
    Truran, M., Schmakeit, J., Ashman, H.: The effect of user intent on the stability of search engine results. J. Am. Soc. Inform. Sci. Technol. 62(7), 1276–1287 (2011)CrossRefGoogle Scholar
  70. 70.
    Vickery, A., Brooks, H.: Plexus: the expert system for referral. Inf. Process. Manage. 23(2), 99–117 (1987)CrossRefGoogle Scholar
  71. 71.
    Voorhees, M.E.: On test collections for adaptive information retrieval. Inf. Process. Manage. 44(6), 1879–1885 (2008)CrossRefGoogle Scholar
  72. 72.
    Xie, Y., Raghavan, V.V.: Language-modeling kernel based approach for information retrieval. J. Am. Soc. Inform. Sci. Technol. 58(14), 2353–2365 (2007)CrossRefGoogle Scholar
  73. 73.
    Zanker, M., Jessenitschnig, M.: Case-studies on exploiting explicit customer requirements in recommender systems. User Model. User-Adap. Inter. 19(1–2), 133–166 (2009)CrossRefGoogle Scholar
  74. 74.
    Zhang, Y.: Complex adaptive filtering user profile using graphical models. Inf. Process. Manage. 44(6), 1886–1900 (2008)MATHCrossRefGoogle Scholar
  75. 75.
    Zhao, Q., Santos, E.J., Nguyen, H., Mohammed, M.: What makes a good summary? In: Argamon, S., Howard, N. (eds.) Computational Methods for Counterterrorism, pp. 33–50. Springer, New York, (2009)Google Scholar

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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