Towards a graph-based user profile modeling for a session-based personalized search

  • Mariam DaoudEmail author
  • Lynda-Tamine Lechani
  • Mohand Boughanem
Regular Paper


Most Web search engines use the content of the Web documents and their link structures to assess the relevance of the document to the user’s query. With the growth of the information available on the web, it becomes difficult for such Web search engines to satisfy the user information need expressed by few keywords. First, personalized information retrieval is a promising way to resolve this problem by modeling the user profile by his general interests and then integrating it in a personalized document ranking model. In this paper, we present a personalized search approach that involves a graph-based representation of the user profile. The user profile refers to the user interest in a specific search session defined as a sequence of related queries. It is built by means of score propagation that allows activating a set of semantically related concepts of reference ontology, namely the ODP. The user profile is maintained across related search activities using a graph-based merging strategy. For the purpose of detecting related search activities, we define a session boundary recognition mechanism based on the Kendall rank correlation measure that tracks changes in the dominant concepts held by the user profile relatively to a new submitted query. Personalization is performed by re-ranking the search results of related queries using the user profile. Our experimental evaluation is carried out using the HARD 2003 TREC collection and showed that our session boundary recognition mechanism based on the Kendall measure provides a significant precision comparatively to other non-ranking based measures like the cosine and the WebJaccard similarity measures. Moreover, results proved that the graph-based search personalization is effective for improving the search accuracy.


Personalization Graph-based user profile Ontology Search session Session boundaries 


  1. 1.
    Alexandru CP, Wolfgang N, Raluca P, Christian K (2005) Using odp metadata to personalize search. In: SIGIR ’05: Proceedings of the 28th annual international ACM SIGIR conference on research and development in information retrieval, pp 178–185Google Scholar
  2. 2.
    Allan J (2003) Hard track overview in trec 2003: high accuracy retrieval from documents. In: TREC, pp 24–37Google Scholar
  3. 3.
    Allan J et al (2003) Challenges in information retrieval and language modeling: report of a workshop held at the center for intelligent information retrieval, University of Massachusetts Amherst, September 2002. SIGIR Forum 37(1): 31–47CrossRefMathSciNetGoogle Scholar
  4. 4.
    Begg IM, Gnocato J, Moore WE (1993) A prototype intelligent user interface for real-time supervisory control systems. In: IUI ’93: Proceedings of the 1st international conference on Intelligent user interfaces. ACM Press, New York, pp 211–214Google Scholar
  5. 5.
    Boughanem M, Chrisment C, Mothe J, Soul-Dupuy C, Tamine L (2000) Connectionist and genetic approaches to achieve IR. In: Crestani F, Gabriella P (eds) Soft computing in information retrieval techniques and applications. Springer, Berlin, pp 173–198Google Scholar
  6. 6.
    Boughanem M, Tamine L (2002) A study on genetic niching for query optimisation in document retrieval. In: Europeen colloquium on information retrieval, Glasgow, 25–27 March 2002, pp 135–149Google Scholar
  7. 7.
    Chen M-S, Park JS, Yu PS (1998) Efficient data mining for path traversal patterns. IEEE Trans Knowl Data Eng 10(2): 209–221CrossRefGoogle Scholar
  8. 8.
    Cooley R, Mobasher B, Srivastava J (1999) Data preparation for mining world wide web browsing patterns. Knowl Inf Syst 1: 5–32Google Scholar
  9. 9.
    Daoud M, Tamine L, Boughanem M (2008) Learning user interests for session-based personalized search. In: ACM Information Interaction in context (IIiX). ACM Press, London, pp 57–64Google Scholar
  10. 10.
    Daoud M, Tamine L, Boughanem M, Chebaro B (2009) A session based personalized search using an ontological user profile. In: ACM symposium on applied computing (SAC), Haiwai (USA). ACM Press, London, pp 1031–1035Google Scholar
  11. 11.
    Ding C, Patra JC, Peng FC (2005) Personalized web search with self-organizing map. In: EEE ’05: Proceedings of the 2005 IEEE international conference on e-Technology, e-Commerce and e-Service (EEE’05) on e-Technology, e-Commerce and e-Service. IEEE Computer Society, Washington, DC, pp 144–147Google Scholar
  12. 12.
    Dumais S, Cadiz ECJJ, Jancke G, Sarin R, Daniel CR (2003) Stuff i’ve seen: a system for personal information retrieval and re-use. In: SIGIR’03: Proceedings of the 26th annual international ACM SIGIR conference on research and development in informaion retrieval (SIGIR ’03). ACM Press, London, pp 72–79Google Scholar
  13. 13.
    Feng Q, Junghoo C (2006) Automatic identification of user interest for personalized search. In: WWW ’06: Proceedings of the 15th international conference on world wide web, pp 727–736Google Scholar
  14. 14.
    Foss A, Wang W, Zaane OR (2001) A non-parametric approach to web log analysis. In: Workshop on webmining in first international SIAM conference on data mining, pp 41–50Google Scholar
  15. 15.
    Gauch S, Chaffee J, Pretschner A (2003) Ontology-based personalized search and browsing. Web Intell Agent Syst 1(3–4): 219–234Google Scholar
  16. 16.
    Gowan J (2003) A multiple model approach to personalised information access. Master thesis in computer science, Faculty of science, Universitt de College DublinGoogle Scholar
  17. 17.
    Haveliwala TH, Gionis A, Klein D, Indyk P (2002) Evaluating strategies for similarity search on the web. In: WWW’02: Proceedings of the eleventh international world wide web conference, pp 432–442Google Scholar
  18. 18.
    He D (2000) Detecting session boundaries from web user logs. In: Proceedings of the BCS-IRSG 22nd annual colloquium on information retrieval research, pp 57–66Google Scholar
  19. 19.
    Huang X, Peng F, An A, Schuurmans D (2004) Dynamic web log session identification with statistical language models. J Am Soc Inf Sci Technol 55(14): 1290–1303CrossRefGoogle Scholar
  20. 20.
    Huang X, Yao Q, An A (2006) Applying language modeling to session identification from database trace logs. Knowl Inf Syst 10(4): 473–504CrossRefGoogle Scholar
  21. 21.
    Jaime T, T., DS, Eric H (2005) Personalizing search via automated analysis of interests and activities. In: SIGIR ’05: Proceedings of the 28th annual international ACM SIGIR conference on research and development in information retrieval, pp 449–456Google Scholar
  22. 22.
    Jansen BJ, Spink A, Kathuria V (2006) How to define searching sessions on web search engines. In: Advances in web mining and web usage analysis, 8th international workshop on knowledge discovery on the web, WebKDD 2006, Philadelphia, pp 92–109Google Scholar
  23. 23.
    Jeh G, Widom J (2003) Scaling personalized web search. In: WWW ’03: Proceedings of the 12th international conference on world wide web. ACM Press, New York, pp 271–279Google Scholar
  24. 24.
    John RI, Mooney GJ (2001) Fuzzy user modeling for information retrieval on the world wide web. Knowl Inf Syst 3(1): 81–95zbMATHCrossRefGoogle Scholar
  25. 25.
    Kelly D, Teevan J (2003) Implicit feedback for inferring user preference: a bibliography. SIGIR Forum 37(2): 18–28CrossRefGoogle Scholar
  26. 26.
    Kim HR, Chan PK (2003) Learning implicit user interest hierarchy for context in personalization. In: IUI ’03: Proceedings of the 8th international conference on Intelligent user interfaces. ACM Press, New York, pp 101–108Google Scholar
  27. 27.
    Koutrika G, Ioannidis Y (2005) A unified user profile framework for query disambiguation and personalization. In: Proceedings of workshop on new technologies for personalized information accessGoogle Scholar
  28. 28.
    Leung CW, Chan SC, Chung F (2006) A collaborative filtering framework based on fuzzy association rules and multiple-level similarity. Knowl Inf Syst 10(3): 357–381CrossRefGoogle Scholar
  29. 29.
    Lieberman H (1995) Letizia: an agent that assists web browsing. In: IJCAI 95: Proceedings of international joint proceedings of the fourteenth international joint conference on artificial intelligence, pp 924–929Google Scholar
  30. 30.
    Lieberman H (1997) Autonomous interface agents. In: CHI, pp 67–74Google Scholar
  31. 31.
    Lin C, Xue G, Zeng H, YU Y (2005) Using probabilistic latent semantic analysis for personalised web search. In: Proceedings of the APWeb conference, pp 707–711Google Scholar
  32. 32.
    Liu F, Yu C, Meng W (2004) Personalized web search for improving retrieval effectiveness. IEEE Trans Knowl Data Eng 16(1): 28–40CrossRefGoogle Scholar
  33. 33.
    Ma Z, Pant G, Sheng ORL (2007) Interest-based personalized search. ACM Trans Inf Syst 25(1): 5CrossRefGoogle Scholar
  34. 34.
    Maguitman AG, Menczer F, Roinestad H, Vespignani A (2005) Algorithmic detection of semantic similarity. In: WWW ’05: Proceedings of the 14th international conference on World Wide Web. ACM Press, New York, pp 107–116Google Scholar
  35. 35.
    Menczer F, Pant G, Srinivasan P (2004) Topical web crawlers: evaluating adaptive algorithms. ACM Trans Interet Technol 4(4): 378–419CrossRefGoogle Scholar
  36. 36.
    Micarelli A, Sciarrone F (2004) Anatomy and empirical evaluation of an adaptive web-based information filtering system. User Model User-Adapt Interact 14(2–3): 159–200CrossRefGoogle Scholar
  37. 37.
    Mobasher B (2007) Data mining for web personalization. In: Brusilovsky P, Kobsa A, Nejdl W (eds) Lecture notes in computer science. Springer, New YorkGoogle Scholar
  38. 38.
    Rocchio JJ (1971) Relevance feedback in information retrieval. In: Salton G. (ed) The SMART retrieval system—experiments in automatic document processing. Prentice-Hall, Englewood CliffsGoogle Scholar
  39. 39.
    Shahabi C, Chen Y-S (2003) Web information personalization: Challenges and approaches. In: Bianchi-Berthouze N (ed) DNIS. Lecture notes in computer science, vol 2822. Springer, Berlin, pp 5–15Google Scholar
  40. 40.
    Shen D, Chen Z, Yang Q, Zeng H-J, Zhang B, Lu Y, Ma W-Y (2004) Web-page classification through summarization. In: SIGIR ’04: Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval. ACM Press, New York, pp 242–249Google Scholar
  41. 41.
    Shen X, Tan B, Zhai C (2005a) Context-sensitive information retrieval using implicit feedback. In: SIGIR ’05: Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval. ACM Press, New York, pp 43–50Google Scholar
  42. 42.
    Shen X, Tan B, Zhai C (2005b) Implicit user modeling for personalized search. In: CIKM ’05: Proceedings of the 14th ACM international conference on Information and knowledge management. ACM Press, New York, pp 824–831Google Scholar
  43. 43.
    Sieg A, Mobasher B, Burke R (2007) Web search personalization with ontological user profiles. In: CIKM’07: Proceedings of the sixteenth ACM conference on conference on information and knowledge management. ACM Press, New York, pp 525–534Google Scholar
  44. 44.
    Sieg A, Mobasher B, Burke R, Prabu G, Lytinen S (2004) Using concept hierarchies to enhance user queries in web-based information retrieval. In: The IASTED international conference on artificial intelligence and applications. Innsbruck, AustriaGoogle Scholar
  45. 45.
    Spink A, Ozmutlu S, Ozmutlu HC, Jansen BJ (2002) U.S. versus European web searching trends. SIGIR Forum 36(2): 32–38CrossRefGoogle Scholar
  46. 46.
    Srinivasan P, Menczer F, Pant G (2005) A general evaluation framework for topical crawlers. Inf Retr 8(3): 417–447CrossRefGoogle Scholar
  47. 47.
    Sriram S, Shen X, Zhai C (2004) A session-based search engine. In: SIGIR’04: Proceedings of the international ACM SIGIR conferenceGoogle Scholar
  48. 48.
    Tamine L, Boughanem M, Daoud M (2009) Evaluation of contextual information retrieval: overview of issues and research. Knowl Inf Syst (Kais) (to appear)Google Scholar
  49. 49.
    Tamine L, Boughanem M, Zemirli WN (2008) Personalized document ranking: exploiting evidence from multiple user interests for profiling and retrieval. J Digit Inf Manage 6(5): 354–365Google Scholar
  50. 50.
    Tan A-H, Ong H-L, Pan H, Ng J, Li Q-X (2004) Towards personalised web intelligence. Knowl Inf Syst 6(5): 595–616CrossRefGoogle Scholar
  51. 51.
    Tan B, Shen X, Zhai C (2006) Mining long-term search history to improve search accuracy. In: KDD ’06: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM Press, New York, pp 718–723Google Scholar
  52. 52.
    Wang W, Zanane OR (2002) Clustering web sessions by sequence alignment. In: Proceedings of the 13th international workshop on database and expert systems applications (DEXA 2002). Springer, Aix-en-Provence, pp 394–398Google Scholar
  53. 53.
    Webb G, Pazzani M, Billsus D (2001) Machine learning for user modeling. User Model User-Adapt Interact 11(1–2): 19–29zbMATHCrossRefGoogle Scholar
  54. 54.
    Wu X, Kumar V, Ross Quinlan J, Ghosh J, Yang Q, Motoda H, McLachlan GJ, Ng A, Liu B, Yu PS, Zhou Z-H, Steinbach M, Hand DJ, Steinberg D (2007) Top 10 algorithms in data mining. Knowl Inf Syst 14(1): 1–37CrossRefGoogle Scholar
  55. 55.
    Zhicheng D, Ruihua S, Ji-Rong W (2007) A large-scale evaluation and analysis of personalized search strategies. In: WWW ’07: Proceedings of the 16th international conference on World Wide Web, pp 581–590Google Scholar
  56. 56.
    Zhou Y, Croft WB (2008) Measuring ranked list robustness for query performance prediction. Knowl. Inf. Syst. 16(2): 155–171CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2009

Authors and Affiliations

  • Mariam Daoud
    • 1
    Email author
  • Lynda-Tamine Lechani
    • 1
  • Mohand Boughanem
    • 1
  1. 1.IRITPaul Sabatier UniversityToulouseFrance

Personalised recommendations