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Communities, Collaboration, and Recommender Systems in Personalized Web Search

  • Barry Smyth
  • Maurice Coyle
  • Peter Briggs
Chapter

Abstract

Web search engines are the primary means by which millions of users access information everyday and the sheer scale and success of the leading search engines is a testimony to the scientific and engineering progress that has been made over the last ten years. However, mainstream search engines continue to deliver largely one-size-fits-all services to their user-base, ultimately limiting the relevance of their result-lists. In this chapter we will explore recent research that is seeking to make Web search a more personal and collaborative experience as we look towards a new breed of more social search engines.

Keywords

Search Engine Recommender System Relevance Feedback Query Term Search Experience 
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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Notes

Acknowledgements

This work is supported by Science Foundation Ireland under grant 07/CE/I1147.

References

  1. 1.
    Saleema Amershi and Meredith Ringel Morris. Cosearch: a system for co-located collaborative web search. In Proceedings of the annual SIGCHI conference on Human factors in computing systems (CHI), pages 1647–1656, 2008.Google Scholar
  2. 2.
    Fabio A. Asnicar and Carlo Tasso. Ifweb: a prototype of user model-based intelligent agent for document filtering and navigation in the world wide web. In Proceedings of the Workshop on Adaptive Systems and User Modeling on the World Wide Web at the Sixth International Conference on User Modeling, pages 3–11, 1997.Google Scholar
  3. 3.
    Ricardo A. Baeza-Yates, Carlos A. Hurtado, and Marcelo Mendoza. Query recommendation using query logs in search engines. In Current Trends in Database Technology - EDBT 2004 Workshops, pages 588–596, 2004.Google Scholar
  4. 4.
    Ricardo A. Baeza-Yates and Berthier A. Ribeiro-Neto. Modern Information Retrieval. ACM Press / Addison-Wesley, 1999.Google Scholar
  5. 5.
    Lori Baker-Eveleth, Suprateek Sarker, and Daniel M. Eveleth. Formation of an online community of practice: An inductive study unearthing key elements. In Proceedings of the 38th Annual Hawaii International Conference on System Sciences, pages 254–256, 2005.Google Scholar
  6. 6.
    M. Balabanovic and Y. Shoham. FAB: Content-Based Collaborative Recommender. Communications of the ACM, 40(3):66–72, 1997.CrossRefGoogle Scholar
  7. 7.
    Evelyn Balfe and Barry Smyth. Improving web search through collaborative query recommendation. In Proceedings of the European Conference on Artificial Intelligence (ECAI, pages 268–272, 2004.Google Scholar
  8. 8.
    D. Billsus and M. Pazzani. A Hybrid User Model for News Story Classification. In Proceedings of the Seventh International Conference on User Modeling, UM ’99, 1999. Banff, Canada.Google Scholar
  9. 9.
    Daniel Billsus, Michael J. Pazzani, and James Chen. A learning agent for wireless news access. In IUI ’00: Proceedings of the 5th international conference on Intelligent user interfaces, pages 33–36, New York, NY, USA, 2000. ACM Press.Google Scholar
  10. 10.
    Oisín Boydell and Barry Smyth. Enhancing case-based, collaborative web search. In Proceedings of International Conference on Case-Based Reasoning (ICCBR), pages 329–343, 2007.Google Scholar
  11. 11.
    John G. Breslin, Andreas Harth, Uldis Bojars, and Stefan Decker. Towards semanticallyinterlinked online communities. In European Semantic Web Conference (ESWC), pages 500– 514, 2005.Google Scholar
  12. 12.
    Peter Briggs and Barry Smyth. Provenance, trust, and sharing in peer-to-peer case-based web search. In Proceedings of European Conference on Case-Based Reasoning (ECCBR), pages 89–103, 2008.Google Scholar
  13. 13.
    Sergey Brin and Lawrence Page. The anatomy of a large-scale hypertextual web search engine. Comput. Netw. ISDN Syst., 30(1-7):107–117, 1998.Google Scholar
  14. 14.
    Jay Budzik and Kristian J. Hammond. User interactions with everyday applications as context for just-in-time information access. In IUI ’00: Proceedings of the 5th international conference on Intelligent user interfaces, pages 44–51, New York, NY, USA, 2000. ACM.Google Scholar
  15. 15.
    R. Burke. The Wasabi Personal Shopper: A Case-Based Recommender System. In Proceedings of the Seventeenth National Conference on Artificial Intelligence. AAAI Press, 1999.Google Scholar
  16. 16.
    John M. Carroll and Mary Beth Rosson. Paradox of the active user. In John M. Carroll, editor, Interfacing Thought: Cognitive Aspects of Human-Computer Interaction,  chapter 5, pages 80–111. Bradford Books/MIT Press, 1987.
  17. 17.
    Soumen Chakrabarti, Byron Dom, Ravi Kumar, Prabhakar Raghavan, Sridhar Rajagopalan, Andrew Tomkins, David Gibson, and Jon M. Kleinberg. Mining the web’s link structure. IEEE Computer, 32(8):60–67, 1999.Google Scholar
  18. 18.
    Pierre-Antoine Champin, Peter Briggs, Maurice Coyle, and Barry Smyth. Coping with noisy search experiences. In Twenty-ninth SGAI International Conference on Artificial Intelligence (AI-2009). Springer-Verlag, 2009. 610 Barry Smyth, Maurice Coyle and Peter BriggsGoogle Scholar
  19. 19.
    Huan Chang, David Cohn, and Andrew McCallum. Learning to create customized authority lists. In ICML ’00: Proceedings of the Seventeenth International Conference on Machine Learning, pages 127–134, San Francisco, CA, USA, 2000. Morgan Kaufmann Publishers Inc.Google Scholar
  20. 20.
    Li Chen and Pearl Pu. Evaluating critiquing-based recommender agents. In Proceedings of the National Conference on Artificial Intelligence (AAAI), 2006.Google Scholar
  21. 21.
    Alvin Chin and Mark H. Chignell. Identifying active subgroups in online communities. In CASCON, pages 280–283, 2007.Google Scholar
  22. 22.
    Paul Alexandru Chirita,Wolfgang Nejdl, Raluca Paiu, and Christian Kohlsch¨utter. 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, pages 178– 185, New York, NY, USA, 2005. ACM Press.Google Scholar
  23. 23.
    Paul-Alexandru Chirita, Daniel Olmedilla, and Wolfgang Nejdl. Pros: A personalized ranking platform for web search. In Paul De Bra and Wolfgang Nejdl, editors, Proceedings of International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems (AH), volume 3137 of Lecture Notes in Computer Science, pages 34–43. Springer, 2004.Google Scholar
  24. 24.
    Maurice Coyle and Barry Smyth. Information recovery and discovery in collaborative web search. In Proceedings of the European COnference on Information retrieval (ECIR), pages 356–367, 2007.Google Scholar
  25. 25.
    Maurice Coyle and Barry Smyth. Supporting intelligent web search. ACM Trans. Internet Techn., 7(4), 2007.Google Scholar
  26. 26.
    W. Bruce Croft, Stephen Cronen-Townsend, and Victor Larvrenko. Relevance feedback and personalization: A language modeling perspective. In DELOS Workshop: Personalisation and Recommender Systems in Digital Libraries, 2001.Google Scholar
  27. 27.
    B.J. Dahlen, J.A. Konstan, J.L. Herlocker, N. Good, A. Borchers, and J. Riedl. Jump-starting movieLens: User benefits of starting a collaborative filtering system with ”dead-data”. In . University of Minnesota TR 98-017, 1998.Google Scholar
  28. 28.
    B. V. Dasarathy. Nearest Neighbor (NN) Norms: NN Pattern Classification Techniques. IEEE Computer Society Press, Los Alamitos, CA, 1991.Google Scholar
  29. 29.
    R. Dolin, D. Agrawal, A. El Abbadi, and L. Dillon. Pharos: a scalable distributed architecture for locating heterogeneous information sources. In CIKM ’97: Proceedings of the sixth international conference on Information and knowledge management, pages 348–355, New York, NY, USA, 1997. ACM.Google Scholar
  30. 30.
    Susan Feldman and Chris Sherman. The High Cost of Not Finding Information. In (IDC White Paper). IDC Group, 2000.Google Scholar
  31. 31.
    L. Finkelstein, E. Gabrilovich, Y. Matias, E. Rivlin, Z. Solan, G. Wolfman, and E. Ruppin. Placing search in context: the concept revisited. In WWW ’01: Proceedings of the 10th International Conference on the World Wide Web, pages 406–414. ACM Press, 2001.Google Scholar
  32. 32.
    C. Lee Giles, Kurt D. Bollacker, and Steve Lawrence. Citeseer: an automatic citation indexing system. In DL ’98: Proceedings of the third ACM conference on Digital libraries, pages 89– 98, New York, NY, USA, 1998. ACM.Google Scholar
  33. 33.
    Laura A. Granka, Thorsten Joachims, and Geri Gay. Eye-tracking analysis of user behavior in www search. In SIGIR ’04: Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval, pages 478–479, New York, NY, USA, 2004. ACM.Google Scholar
  34. 34.
    Luis Gravano, H´ector García-Molina, and Anthony Tomasic. Gloss: text-source discovery over the internet. ACM Trans. Database Syst., 24(2):229–264, 1999.CrossRefGoogle Scholar
  35. 35.
    Anthony Jameson and Barry Smyth. Recommendation to groups. In Peter Brusilovsky, Alfred Kobsa, and Wolfgang Nejdl, editors, The Adaptive Web, pages 596–627. Springer-Verlag, 2007.Google Scholar
  36. 36.
    Bernard J. Jansen and Amanda Spink. An analysis of web searching by european alltheweb.com users. Inf. Process. Manage., 41(2):361–381, 2005.CrossRefGoogle Scholar
  37. 37.
    Bernard J. Jansen, Amanda Spink, Judy Bateman, and Tefko Saracevic. Real life information retrieval: a study of user queries on the web. SIGIR Forum, 32(1):5–17, 1998.CrossRefGoogle Scholar
  38. 38.
    Glen Jeh and Jennifer Widom. Scaling personalized web search. In WWW ’03: Proceedings of the 12th international conference on World Wide Web, pages 271–279, New York, NY, USA, 2003. ACM.Google Scholar
  39. 39.
    Jon M. Kleinberg. Authoritative sources in a hyperlinked environment. J. ACM, 46(5):604– 632, 1999.MATHCrossRefMathSciNetGoogle Scholar
  40. 40.
    Jon M. Kleinberg. Hubs, authorities, and communities. ACM Comput. Surv., 31(4):5, 1999.CrossRefGoogle Scholar
  41. 41.
    J.A. Konstan, B.N Miller, D. Maltz, J.L. Herlocker, L.R. Gorgan, and J. Riedl. GroupLens: Applying collaborative filtering to Usenet news. Communications of the ACM, 40(3):77–87, 1997.CrossRefGoogle Scholar
  42. 42.
    Yehuda Koren. Tutorial on recent progress in collaborative filtering. In Proceedings of the International Conference on Recommender Systems (RecSys), pages 333–334, 2008.Google Scholar
  43. 43.
    G. Koutrika and Y. Ioannidis. A unified user-profile framework for query disambiguation and personalization. In Proc. of the Workshop on New Technologies for Personalized Information Access, pages 44–53, 2005.Google Scholar
  44. 44.
    A. Kruger, C. L. Giles, F. M. Coetzee, E. Glover, G. W. Flake, S. Lawrence, and C. Omlin. Deadliner: building a new niche search engine. In CIKM ’00: Proceedings of the ninth international conference on Information and knowledge management, pages 272–281, New York, NY, USA, 2000. ACM.Google Scholar
  45. 45.
    S. Lawrence and C. Lee Giles. Accessibility of Information on the Web. Nature, 400(6740):107–109, 1999.CrossRefGoogle Scholar
  46. 46.
    Greg Linden, Brent Smith, and Jeremy York. Industry report: Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Distributed Systems Online, 4(1), 2003.Google Scholar
  47. 47.
    Fang Liu, Clement Yu, andWeiyi Meng. Personalized web search by mapping user queries to categories. In CIKM ’02: Proceedings of the eleventh international conference on Information and knowledge management, pages 558–565, New York, NY, USA, 2002. ACM Press.Google Scholar
  48. 48.
    Zhongming Ma, Gautam Pant, and Olivia R. Liu Sheng. Interest-based personalized search. ACM Trans. Inf. Syst., 25(1):5, 2007.CrossRefGoogle Scholar
  49. 49.
    Christos Makris, Yannis Panagis, Evangelos Sakkopoulos, and Athanasios Tsakalidis. Category ranking for personalized search. Data Knowl. Eng., 60(1):109–125, 2007.CrossRefGoogle Scholar
  50. 50.
    Gary Marchionini. Exploratory search: from finding to understanding. Communications of the ACM, 49(4):41–46, 2006.CrossRefGoogle Scholar
  51. 51.
    Kevin McCarthy, James Reilly, Lorraine McGinty, and Barry Smyth. Experiments in dynamic critiquing. In Proceedings of the International Conference on Intelligent User Interfaces (IUI), pages 175–182, 2005.Google Scholar
  52. 52.
    Kevin McCarthy, Maria Salam´o, Lorcan Coyle, Lorraine McGinty, Barry Smyth, and Paddy Nixon. Cats: A synchronous approach to collaborative group recommendation. In Proceedings of the International FLAIRS Conference, pages 86–91, 2006.Google Scholar
  53. 53.
    L. McGinty and B. Smyth. Comparison-Based Recommendation. In Susan Craw, editor, Proceedings of the Sixth European Conference on Case-Based Reasoning (ECCBR 2002), pages 575–589. Springer, 2002. Aberdeen, Scotland.Google Scholar
  54. 54.
    Ryan J. Meuth, Paul Robinette, and Donald C. Wunsch. Computational intelligence meets the netflix prize. In Proceedings of the International Joint Conference on Neural Networks (IJCNN), pages 686–691, 2008.Google Scholar
  55. 55.
    Alessandro Micarelli and Filippo Sciarrone. Anatomy and empirical evaluation of an adaptive web-based information filtering system. User Modeling and User-Adapted Interaction, 14(2- 3):159–200, 2004.Google Scholar
  56. 56.
    M. Mitra, A. Singhal, and C. Buckley. Improving Automatic Query Expansion. In Proceedings of ACM SIGIR, pages 206–214. ACM Press, 1998.Google Scholar
  57. 57.
    Meredith Ringel Morris. A survey of collaborative web search practices. In Proceedings of the annual SIGCHI conference on Human factors in computing systems (CHI), pages 1657– 1660, 2008.Google Scholar
  58. 58.
    Meredith Ringel Morris and Eric Horvitz. S3: Storable, shareable search. In INTERACT (1), pages 120–123, 2007.Google Scholar
  59. 59.
    Meredith Ringel Morris and Eric Horvitz. Searchtogether: an interface for collaborative web search. In UIST, pages 3–12, 2007.Google Scholar
  60. 60.
    Sridhar P. Nerur, Riyaz Sikora, George Mangalaraj, and Venugopal Balijepally. Assessing the relative influence of journals in a citation network. Commun. ACM, 48(11):71–74, 2005.CrossRefGoogle Scholar
  61. 61.
    Vicki L. O’Day and Robin Jeffries. Orienteering in an information landscape: how information seekers get from here to there. In CHI ’93: Proceedings of the SIGCHI conference on Human factors in computing systems, pages 438–445, New York, NY, USA, 1993. ACM Press.Google Scholar
  62. 62.
    John O’Donovan, Vesile Evrim, and Barry Smyth. Personalizing trust in online auctions. In Proceedings of the European Starting AI Researcher Symposium (STAIRS), Trento, Italy, 2006.Google Scholar
  63. 63.
    John O’Donovan and Barry Smyth. Eliciting trust values from recommendation errors. In Proceedings of the International FLAIRS Conference, pages 289–294, 2005.Google Scholar
  64. 64.
    John O’Donovan and Barry Smyth. Trust in recommender systems. In Proceedings of the International Conference on Intelligent User Interfaces (IUI), pages 167–174, 2005.Google Scholar
  65. 65.
    John O’Donovan and Barry Smyth. Trust no one: Evaluating trust-based filtering for recommenders. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), pages 1663–1665, 2005.Google Scholar
  66. 66.
    John O’Donovan and Barry Smyth. Is trust robust?: an analysis of trust-based recommendation. In Intelligent User Interfaces, pages 101–108, 2006.Google Scholar
  67. 67.
    John O’Donovan and Barry Smyth. Mining trust values from recommendation errors. International Journal on Artificial Intelligence Tools, 15(6):945–962, 2006.CrossRefGoogle Scholar
  68. 68.
    Jeremy Pickens, Gene Golovchinsky, Chirag Shah, Pernilla Qvarfordt, and Maribeth Back. Algorithmic mediation for collaborative exploratory search. In SIGIR, pages 315–322, 2008.Google Scholar
  69. 69.
    Alexander Pretschner and Susan Gauch. Ontology based personalized search. In ICTAI ’99: Proceedings of the 11th IEEE International Conference on Tools with Artificial Intelligence, page 391, Washington, DC, USA, 1999. IEEE Computer Society.Google Scholar
  70. 70.
    Madhu C. Reddy and Paul Dourish. A finger on the pulse: temporal rhythms and information seeking in medical work. In CSCW, pages 344–353, 2002.Google Scholar
  71. 71.
    Madhu C. Reddy, Paul Dourish, and Wanda Pratt. Coordinating heterogeneous work: Information and representation in medical care. In ECSCW, pages 239–258, 2001.Google Scholar
  72. 72.
    Madhu C. Reddy and Bernard J. Jansen. A model for understanding collaborative information behavior in context: A study of two healthcare teams. Inf. Process. Manage., 44(1):256–273, 2008.CrossRefGoogle Scholar
  73. 73.
    Madhu C. Reddy and Patricia Ruma Spence. Collaborative information seeking: A field study of a multidisciplinary patient care team. Inf. Process. Manage., 44(1):242–255, 2008.CrossRefGoogle Scholar
  74. 74.
    J. Reilly, K. McCarthy, L. McGinty, and B. Smyth. Dynamic Critiquing. In Peter Funk and Pedro A. Gonzalez Calero, editors, Proceedings of the 7th European Conference on Case- Based Reasoning, pages 763–777. Springer-Verlag, 2004.Google Scholar
  75. 75.
    James Reilly, Kevin McCarthy, Lorraine McGinty, and Barry Smyth. Incremental critiquing. Knowl.-Based Syst., 18(4-5):143–151, 2005.CrossRefGoogle Scholar
  76. 76.
    James Reilly, Barry Smyth, Lorraine McGinty, and Kevin McCarthy. Critiquing with confidence. In Proceedings of International Conference on Case-Based Reasoning (ICCBR), pages 436–450, 2005.Google Scholar
  77. 77.
    Paul Resnick and Hal R. Varian. Recommender systems. Commun. ACM, 40(3):56–58, 1997.CrossRefGoogle Scholar
  78. 78.
    J. Rocchio. Relevance Feedback in Information Retrieval. G. Salton (editor), The SMART Retrieval System: Experiments in Automatic Document Processing. Prentice–Hall, Inc., Englewood Cliffs, NJ, 1971.Google Scholar
  79. 79.
    U. Rohini and Vasudeva Varma. A novel approach for re-ranking of search results using collaborative filtering. In Proceedings of the International Conference on Computing: Theory and Applications, volume 00, pages 491–496, Los Alamitos, CA, USA, 2007. IEEE Computer Society.Google Scholar
  80. 80.
    Lior Rokach, Genetic algorithm-based feature set partitioning for classification problems, Pattern Recognition, 41(5):1676–1700, 2008. 18 Personalized Web Search 613Google Scholar
  81. 81.
    Mehran Sahami and Timothy D. Heilman. A web-based kernel function for measuring the similarity of short text snippets. In Proceedings of the International World-Wide Web Conference, pages 377–386, 2006.Google Scholar
  82. 82.
    Gerard Salton and Chris Buckley. Improving retrieval performance by relevance feedback. In Readings in information retrieval, pages 355–364. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 1997.Google Scholar
  83. 83.
    J. Ben Schafer, Joseph Konstan, and John Riedi. Recommender systems in e-commerce. In EC ’99: Proceedings of the 1st ACM conference on Electronic commerce, pages 158–166, New York, NY, USA, 1999. ACM Press.Google Scholar
  84. 84.
    U. Shardanand and P. Maes. Social Information Filtering: Algorithms for Automating ”Word of Mouth”. In Proceedings of the Conference on Human Factors in Computing Systems (CHI ’95), pages 210–217. ACM Press, 1995. New York, USA.Google Scholar
  85. 85.
    U. Shardanand and P. Maes. Social Information Filtering: Algorithms for Automating ”Word of Mouth”. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI), pages 210–217, 1995.Google Scholar
  86. 86.
    Xuehua Shen, Bin Tan, and ChengXiang Zhai. Implicit User Modeling for Personalized Search. In Proceedings of the Fourteenth ACM Conference on Information and Knowledge Management (CIKM 05), 2005.Google Scholar
  87. 87.
    Alan F. Smeaton, Colum Foley, Daragh Byrne, and Gareth J. F. Jones. ibingo mobile collaborative search. In CIVR, pages 547–548, 2008.Google Scholar
  88. 88.
    Alan F. Smeaton, Hyowon Lee, Colum Foley, and Sin´ead McGivney. Collaborative video searching on a tabletop. Multimedia Syst., 12(4-5):375–391, 2007.CrossRefGoogle Scholar
  89. 89.
    Barry Smyth. Case-based recommendation. In Peter Brusilovsky, Alfred Kobsa, and Wolfgang Nejdl, editors, The Adaptive Web, pages 342–376. Springer-Verlag, 2007.Google Scholar
  90. 90.
    Barry Smyth. A community-based approach to personalizing web search. IEEE Computer, 40(8):42–50, 2007.Google Scholar
  91. 91.
    Barry Smyth, Evelyn Balfe, Oisín Boydell, Keith Bradley, Peter Briggs, Maurice Coyle, and Jill Freyne. A Live-user Evaluation of Collaborative Web Search. In Proceedings of the 19th International Joint Conference on Artificial Intelligence (IJCAI ’05), pages 1419–1424. Morgan Kaufmann, 2005. Ediburgh, Scotland.Google Scholar
  92. 92.
    Barry Smyth, Evelyn Balfe, Jill Freyne, Peter Briggs, Maurice Coyle, and Oisín Boydell. Exploiting query repetition and regularity in an adaptive community-based web search engine. User Model. User-Adapt. Interact., 14(5):383–423, 2004.CrossRefGoogle Scholar
  93. 93.
    Barry Smyth, Peter Briggs, Maurice Coyle, and Michael P O’Mahony. A case-based perspective on social web search. In Proceedings of International Conference on Case-Based Reasoning (ICCBR), 2009.Google Scholar
  94. 94.
    Barry Smyth, Peter Briggs, Maurice Coyle, and Michael P. O’Mahony. Google. shared! a case-study in social search. In Proceedings of the International Conference on User Modeling, Adaptation and Personalization (UMAP), pages 494–508. Springer-Verlag, 2009.Google Scholar
  95. 95.
    Barry Smyth and Pierre-Antoine Champin. The experience web: A case-based reasoning perspective. In Workshop on Grand Challenges for Reasoning from Experiences (IJCAI 2009), 2009.Google Scholar
  96. 96.
    Karen Sparck Jones. A statistical interpretation of term specificity and its application in retrieval. In Document retrieval systems, pages 132–142. Taylor Graham Publishing, London, UK, UK, 1988.Google Scholar
  97. 97.
    Micro Speretta and Susan Gauch. Personalized search based on user search histories. In WI ’05: Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence, pages 622–628, Washington, DC, USA, 2005. IEEE Computer Society.Google Scholar
  98. 98.
    Amanda Spink, Judy Bateman, and Major Bernard Jansen. Searching heterogeneous collections on the web: behaviour of excite users. Information Research: An Electronic Journal, 4(2), 1998.Google Scholar
  99. 99.
    Amanda Spink and Bernard J. Jansen. A study of web search trends. Webology, 1(2):4, 2004.Google Scholar
  100. 100.
    Amanda Spink, Dietmar Wolfram, Major B. J. Jansen, and Tefko Saracevic. Searching the Web: the Public and their Queries. Journal of the American Society for Information Science, 52(3):226–234, 2001.CrossRefGoogle Scholar
  101. 101.
    Kazunari Sugiyama, Kenji Hatano, and Masatoshi Yoshikawa. Adaptive web search based on user profile constructed without any effort from users. In WWW ’04: Proceedings of the 13th international conference on World Wide Web, pages 675–684, New York, NY, USA, 2004. ACM Press.Google Scholar
  102. 102.
    Jian-Tao Sun, Hua-Jun Zeng, Huan Liu, Yuchang Lu, and Zheng Chen. Cubesvd: a novel approach to personalized web search. In WWW ’05: Proceedings of the 14th international conference on World Wide Web, pages 382–390, New York, NY, USA, 2005. ACM Press.Google Scholar
  103. 103.
    Bin Tan, Xuehua Shen, and ChengXiang Zhai. 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, pages 718–723, New York, NY, USA, 2006. ACM Press.Google Scholar
  104. 104.
    C. J. van Rijsbergen. Information Retrieval. Butterworth, 1979.Google Scholar
  105. 105.
    Xin Wang and Ata Kab´an. State aggregation in higher order markov chains for finding online communities. In IDEAL, pages 1023–1030, 2006.Google Scholar
  106. 106.
    Xin Wang and Ata Kab´an. A dynamic bibliometric model for identifying online communities. Data Min. Knowl. Discov., 16(1):67–107, 2008.CrossRefMathSciNetGoogle Scholar
  107. 107.
    Yiming Yang and Christopher G. Chute. An example-based mapping method for text categorization and retrieval. ACM Trans. Inf. Syst., 12(3):252–277, 1994.CrossRefGoogle Scholar
  108. 108.
    Baoyao Zhou, Siu Cheung Hui, and Alvis C. M. Fong. An effective approach for periodic web personalization. In WI ’06: Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence, pages 284–292, Washington, DC, USA, 2006. IEEE Computer Society.Google Scholar

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© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.CLARITY: Centre for Sensor Web Technologies, School of Computer Science & InformaticsUniversity College DublinDublinIreland

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