Advertisement

Artificial Intelligence Review

, Volume 18, Issue 1, pp 33–74 | Cite as

User Modeling for Personalized City Tours

  • Josef Fink
  • Alfred Kobsa
Article

Abstract

Several current support systems for travel and tourism are aimed at providing information in a personalized manner, taking users' interests and preferences into account. In this vein, personalized systems observe users' behavior and, based thereon, make generalizations and predictions about them. This article describes a user modeling server that offers services to personalized systems with regard to the analysis of user actions, the representation of assumptions about the user, and the inference of additional assumptions based on domain knowledge and characteristics of similar users. The system is open and compliant with major standards, allowing it to be easily accessed by clients that need personalization services.

interest profile LDAP learning about the user mobile tourist guide personalization user modeling server 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Abowd, G. D., Atkeson, C. G., Hong, J., Long, S., Kooper, R. & Pinkerton, M. (1997). Cyberguide: A Mobile Context-Aware Tour Guide. Wireless Networks 3: 421–433.Google Scholar
  2. Alspector, J., Kolcz, A. & Karunanithi, N. (1997). Feature-Based and Clique-Based User Models for Movie Selection: A Comparative Study. User Modeling and User-Adapted Interaction 7(4): 279–304.Google Scholar
  3. ATG (2001). ATG Dynamo e-Business Platform, Art Technology Group. http://www.atg.com/products/.Google Scholar
  4. Balabanovic, M. (1997). An AdaptiveWeb Page Recommendation Service. First International Conference on Autonomous Agents, 378-385. Marina del Rey, CA.Google Scholar
  5. Balabanovic, M. & Shoham, Y. (1997). Fab: Content-Based, Collaborative Recommendation. Communications of the ACM 40(3): 66–72.Google Scholar
  6. Baltimore (2001). Baltimore Select Access, Baltimore Technologies. http://www.baltimore.com/selectaccess/index.html. Bigfoot (2001). Bigfoot International. http://www.bigfoot.com.Google Scholar
  7. Billsus, D. & Pazzani, M. J. (2000). User Modeling for Adaptive News Access. User Modeling and User-Adapted Interaction 10(2-3): 147–180.Google Scholar
  8. Brajnik, G. & Tasso, C. (1994). A Shell for Developing Non-monotonic User Modeling Systems. International Journal of Human-Computer Studies 40: 31–62.Google Scholar
  9. Breese, J., Heckerman, D. & Kadie, C. (1998). Empirical Analysis of Predictive Algorithms for Collaborative Filtering. Proc. of the Fourteenth Annual Conference on Uncertainty in Artificial Intelligence (UAI-98), 43–52. San Francisco, Morgan Kaufmann.Google Scholar
  10. Carberry, S. (ed.) (2000). User Modeling and User-Adapted Interaction 10(2-3). Special Issue on Deployed User Modeling. Dordrecht, Netherlands, Kluwer Academic Publishers.Google Scholar
  11. Card, S. K., Mackinlay, J. D. & Shneiderman, B. (1999). Information Visualization. In: Card, S. K., Mackinlay J. D. & Shneiderman, B. (eds.) Readings in Information Visualization: Using Vision to Think, 1–34. San Francisco, CA, Morgan Kaufmann.Google Scholar
  12. Carroll, J. M. (ed.) (1995). Scenario-based Design: Envisioning Work and Technology in System Development. New York, NY, John Wiley and Sons.Google Scholar
  13. Carroll, J. M. (ed.) (2000). Making Use: Scenario-based Design of Human-Computer Interactions. Cambridge, MA, MIT Press.Google Scholar
  14. Castano, S., Fugini, M., Martella, G. & Samarati, P. (1995). Database Security. Reading, MA, AddisonWesley.Google Scholar
  15. Chadwick, D. W. (1996). Understanding X.500: The Directory. London, Thomson.Google Scholar
  16. Cheverst, K., Davies, N., Mitchell, K., Friday, A. & Efstratiou, C. (2000a). Developing a Context-aware Electronic Tourist Guide: Some Issues and Experiences, CHI 2000, ACH Conference on Human Factors in Computing Systems, 17-24. The Hague, Netherlands. http://www.comp.lancs.ac.uk/computing/users/kc/Papers/CHI-cheverst.pdf.Google Scholar
  17. Cheverst, K., Davies, N., Mitchell, K. & Smith, P. (2000b). Providing Tailored (Context-Aware) Information to City Visitors. International Conference on Adaptive Hypermedia and Adaptive Webbased Systems, AH 2000, 20-31. Trento, Italy. http://link.springer.de/ link/service/series/0558/papers/1892/18920073.pdf.Google Scholar
  18. Cheverst, K., Mitchell, K. & Davies, N. (2002). The Role of Adaptive Hypermedia within a Context-Aware Tourist GUIDE. Communications of the ACM, May 2002.Google Scholar
  19. Coors, V., Kray, C. & Porzel, R. (2000). Zu komplexen Diensten mit einfachen natürlichsprachlichen Interaktionen. First International Workshop on Digital Storytelling, Darmstadt, Germany, ZGDV. http://www.eml.villa-bosch.de/english/staff/homes/porzel%20publications/15-DISTEL.pdf.Google Scholar
  20. Cost, S. & Salzberg, S. (1993). A weighted nearest neighbor algorithm for learning with symbolic features. Machine Learning 10: 57–78.Google Scholar
  21. Cox, R., O'Donnell, M. & Oberlander, J. (1999). Dynamic versus Static Hypermedia in Museum Education: an Evaluation of ILEX, the Intelligent Labelling Explorer. Artificial Intelligence in Education Conference, Le Mans, France.Google Scholar
  22. Deep Map (2001). Deep Map: Intelligent, Mobile, Multi-Media and Full of Knowledge (Project Homepage). European Media Laboratory. http://www.eml.org/english/research/ deepmap/deepmap.html.Google Scholar
  23. Duda, R. & Hart, P. (1973). Pattern Classification and Scene Analysis. New York, NY, Wiley and Sons.Google Scholar
  24. EML (1999). Annual Report 1998/1999. European Media Laboratory, Heidelberg, Germany.Google Scholar
  25. EML (2000). EML Annual Report 2000. European Media Laboratory, Heidelberg, Germany. http://www.eml.villa-bosch.de/deutsch/aktuell/EML_(2000).pdf.Google Scholar
  26. Fink, J. (1999). Transactional Consistency in User Modeling Systems. In: Kay, J. (ed.) UM99 User Modeling: Proceedings of the Seventh International Conference, 191–200. Wien New York, Springer-Verlag. http://www.um.org.Google Scholar
  27. Fink, J. (2002). User Modeling Servers: Requirements, Design, and Implementation. Ph.D. Thesis, Dept. of Mathematics and Computer Science, University of Essen, Germany (to appear).Google Scholar
  28. Fink, J. & Kobsa, A. (2000). A Review and Analysis of Commercial User Modeling Servers for Personalization on theWorldWideWeb. User Modeling and User-Adapted Interaction 10(2-3): 209–249.Google Scholar
  29. Fink, J., Kobsa, A. & Jaceniak, I. (1997). Individualisierung von Benutzerschnittstellen mit Hilfe von Datenchips für Personalisierungsinformation. GMD-Spiegel 1: 16–17. http://www.ics.uci.edu/~kobsa/papers/1997-GMD-kobsa.pdf.Google Scholar
  30. Fink, J., Kobsa, A. & Nill, A. (1996). User-oriented Adaptivity and Adaptability in the AVANTI Project. Conference. Designing for the Web: Empirical Studies. Redmond, WA. http://www.ics.uci.edu/~kobsa/papers/1996-designing-web-kobsa.pdf.Google Scholar
  31. Fink, J., Kobsa, A. & Nill, A. (1998). Adaptable and Adaptive Information Provision for All Users, Including Disabled and Elderly People. The New Review of Hypermedia and Multimedia 4: 163–188. http://www.ics.uci.edu/~kobsa/papers/1998-NRHM-kobsa.pdf.Google Scholar
  32. FIPA (1998a). FIPA 98 Specification Part 1: Agent Management, Foundation for Intelligent Physical Agents (FIPA). Geneva, Switzerland. http://www.fipa.org.Google Scholar
  33. FIPA (1998b). FIPA 98 Specification Part 8: Human Agent Interaction, Foundation for Intelligent Physical Agents (FIPA). Geneva, Switzerland. http://www.fipa.org.Google Scholar
  34. Fisher, D. (1996). Iterative Optimization and Simplification of Hierarchical Clusterings. Journal of Artificial Intelligence Research 4: 147–179.Google Scholar
  35. Fisher, D. H. (1987). Knowledge Acquisition Via Incremental Conceptual Clustering. Machine Learning 2(2): 139–172.Google Scholar
  36. Gawor, J. (1999). LDAP Browser/Editor. http://www-unix.mcs.anl.gov/~gawor/ldap.Google Scholar
  37. Goldberg, D., Nichols, D., Oki, B. M. & Terry, D. (1992). Using Collaborative Filtering to Weave an Information Tapestry. Communications of the ACM 35(12): 61–70.Google Scholar
  38. Grant, G. (1997). Understanding Digital Signatures: Establishing Trust over the Internet and Other Networks. New York, NY, McGraw-Hill.Google Scholar
  39. Herlocker, J., Konstan, J., Borchers, A. & Riedl, J. (1999). An Algorithmic Framework for Performing Collaborative Filtering. Proc. of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 230-237. New York. http://www.cs.umn.edu/Research/GroupLens/algs.pdf.Google Scholar
  40. Hill, W., Stead, L., Rosenstein, M. & Furnas, G. (1995). Recommending and evaluating choices in a virtual community of use. ACM CHI'95 Conference on Human Factors in Computing Systems, 194-201. New York, NY.Google Scholar
  41. Howes, T., Smith, M. & Good, G. (1999). Understanding and Deploying LDAP Directory Services. Indianapolis, IN, Macmillan.Google Scholar
  42. ILEX (1998). ILEX Virtual Gallerie 2.0. http://www.cstr.ed.ac.uk/cgi-bin/ilex.cgi. Inprise (2001). Inprise. www.inprise.com.Google Scholar
  43. security/dir_srvr.Google Scholar
  44. ITU-T (1993). X.500 Part 1 Overview of Concepts, Models and Services, ISO/IEC Standard 9594.Google Scholar
  45. Jennings, A. & Higuchi, H. (1993). A User Model Neural Network for a Personal News Service. User Modeling and User-Adapted Interaction 3(1): 1–25.Google Scholar
  46. Kaul, E. (1999). Soziokulturelle Kategorisierung der Touristen in Heidelberg, Master Thesis, University of Heidelberg, Heidelberg, Germany.Google Scholar
  47. Kay, J. (1995). The um Toolkit for Reusable, Long Term User Models. User Modeling and User-Adapted Interaction 4(3): 149–196.Google Scholar
  48. Kobsa, A. (2001a). Generic User Modeling Systems. User Modeling and User-Adapted Interaction 11(1-2): 49–63.Google Scholar
  49. Kobsa, A. (ed.) (2001b). User Modeling and User-Adapted Interaction 11(1-2). Ten Year Anniversary Issue. Dordrecht, Netherlands, Kluwer Academic Publishers. http://umuai.informatik.uni-essen.de/anniversary.html.Google Scholar
  50. Kobsa, A., Koenemann, J. & Pohl, W. (2001). Personalized Hypermedia Presentation Techniques for Improving Customer Relationships. The Knowledge Engineering Review 16(2): 111–155. http://www.ics.uci.edu/~kobsa/papers/2001-KER-kobsa.pdf.Google Scholar
  51. Kobsa, A., Müller D. & Nill, A. (1994). KN-AHS: An Adaptive Hypertext Client of the User Modeling System BGP-MS. Proceedings of the Fourth International Conference on User Modeling, 99-105. Hyannis, MA. http://www.ics.uci.edu/~kobsa/papers/1994-UM94-kobsa.pdf.Google Scholar
  52. Kobsa, A. & Pohl, W. (1995). The BGP-MS User Modeling System. User Modeling and User-Adapted Interaction 4(2): 59–106.Google Scholar
  53. Kobsa, A. & Schreck, J. (2002). Privacy through Pseudonymity in User-Adaptive Systems. Submitted.Google Scholar
  54. Kohl, J. & Neuman, C. (1993). The Kerberos Network Authentication Service (Version 5).Google Scholar
  55. Konstan, J. A., Miller, B. N., Maltz, D., Herlocker, J. L., Gordon, L. R. & Riedl, J. (1997). GroupLens: Applying Collaborative Filtering to Usenet News. Communications of the ACM 40(3): 77–87.Google Scholar
  56. Lieberman, H. (1995). Letizia: An Agent that Assists Web Browsing. Fourteenth International Joint Conference on Artificial Intelligence, 924-929. Montreal, Canada.Google Scholar
  57. Macromedia (2001). LikeMinds, Andromedia. http://www.macromedia.com/software/likeminds/.Google Scholar
  58. Malaka, R. (1999). Deep Map: The Multilingual Tourist Guide. C-Star Workshop, Schwetzingen, Germany. http://www.eurescom.de/~pub/fusenetd/Malaka.pdf.Google Scholar
  59. Malaka, R. & Zipf, A. (2000). DEEP MAP - Challenging IT Research in the Framework of a Tourist Information System. In: Fesenmaier, D., Klein, S. & Buhalis, D. (eds.) Information and Communication Technologies in Tourism 2000: Proceedings of ENTER 2000, 15–27. Wien, New York, Springer.Google Scholar
  60. Manna (2001). Frontmind. http://www.mannainc.com/products.html.Google Scholar
  61. Microsoft (2001). Active Directory Architecture. http://www.microsoft.com/windows2000/ techinfo/howitworks/activedirectory/adarch.asp.Google Scholar
  62. Mitchell, T. (1997). Machine Learning. New York, NY, McGraw-Hill.Google Scholar
  63. Net Perceptions (2001). Net Perceptions. http://www.netperceptions.com.Google Scholar
  64. Netegrity (2001). Netegrity SiteMinder, Netegrity Inc. http://www.netegrity.com/products/ index.cfm?leveltwo=SiteMinder.Google Scholar
  65. Nielsen, J. (1996). Top Ten Mistakes in Web Design. http://www.useit.com/alertbox/9605.html.Google Scholar
  66. Not, E., Petrelli, D., Sarini, M., Stock, O., Strapparava, C. & Zancanaro, M. (1998). Hypernavigation in the Physical Space: Adapting Presentations to the User and to the Situational Context. The New Review of Hypermedia and Multimedia 4: 33–45.Google Scholar
  67. Not, E., Petrelli, D., Stock, O., Strapparava, C. & Zancanaro, M. (1997). Person-oriented Guided Visits in a Physical Museum. 4th International Conference on Hypermedia and Interactivity in Museums (ICHIM'97), 162-172. Paris, France.Google Scholar
  68. Novell (2001). Novell NDS eDirectory. http://www.novell.com/products/nds.Google Scholar
  69. Oard, D. W. (1997). The State of the Art in Text Filtering. User Modeling and User-Adapted Interaction 7(3): 141–178.Google Scholar
  70. Oberlander, J.,Mellish, C., O'Donnell, M. & Knott, A. (1997). Exploring a Gallery with Intelligent Labels. 4th International Conference on Hypermedia and Interactivity in Museums (ICHIM'97), 153-161. Paris, France.Google Scholar
  71. OMG (2001). Object Management Group (OMG), OMG. http://www.omg.org.Google Scholar
  72. Oppermann, R. & Specht, M. (1999). A Nomadic Information System for Adaptive Exhibition Guidance. Archives and Museum Informatics 13(2): 127–138. http://fit.gmd.de/~oppi/publications/NomadicInfoSystem.pdf.Google Scholar
  73. Oppermann, R. & Specht, M. (2000). A Context-Sensitive Nomadic Information System as an Exhibition Guide. Proceedings of the Handheld and Ubiquitous Computing Second International Symposium, HUC 2000, 127-142. Bristol, UK.Google Scholar
  74. Orwant, J. (1995). Heterogenous Learning in the Doppelänger User Modeling System. User Modeling and User-Adapted Interaction 4(2): 107–130.Google Scholar
  75. Paiva, A. & Self, J. (1995). TAGUS - A User and Learner Modeling Workbench. User Modeling and User-Adapted Interaction 4(3): 197–226.Google Scholar
  76. Paliouras, G., Karkaletsis, V., Papatheodorou, C. & Spyropoulos, C. (1999). Exploiting Learning Techniques for the Acquisition of User Stereotypes and Communities. In: Kay, J. (ed.) UM99 User Modeling: Proceedings of the Seventh International Conference, 169–178. Wien, New York, Springer-Verlag.Google Scholar
  77. Pazzani, M. & Billsus, D. (1997). Learning and Revising User Profiles: The Identification of Interesting Web Sites. Machine Learning 27: 313–331.Google Scholar
  78. Petrelli, D., Not, E., Sarini, M., Stock, O., Strapparava, C. & Zancanaro, M. (1999). Hyper-Audio: Location-Awareness + Adaptivity. CHI'99, Conference on Human Factors in Computing Systems, 21-22. Extended Abstracts, Pittsburgh, PA.Google Scholar
  79. Pohl, W. (1998). Logic-Based Representation and Reasoning for User Modeling Shell Systems. Sankt Augustin, Germany, infix.Google Scholar
  80. Pohl, W., Schwab, I. & Koychev, I. (1999). Learning About the User: A General Approach and Its Application. IJCAI'99 Workshop Learning About Users. Stockholm, Sweden.Google Scholar
  81. Pope, A. (1997). The Corba Reference Guide: Understanding the Common Object Request Broker Architecture. Sydney, Australia, Addison-Wesley.Google Scholar
  82. Poslad, S., Laamanen, H., M. R., Nick, A., Buckle, P. & Zipf, A. (2001). CRUMPET: Creation of Userfriendly Mobile Services Personalised for Tourism. 3G 2001 - Second International Conference on 3G Mobile Communication Technologies, 26-29. London, England. http://www.emorphia.com/downloads/3g2001-crumpet.pdf.Google Scholar
  83. Quinlan, J. R. (1986). Induction of Decision Trees. Machine Learning 1(1): 81–106.Google Scholar
  84. Reagle, J. & Cranor, L. (1999). The Platform for Privacy Preferences. Communications of the ACM 42(2): 48–55.Google Scholar
  85. Resnick, P., Iacovou, N., Sushak, M., Bergstrom, P. & Riedl, J. (1994). GroupLens: An Open Architecture for Collaborative Filtering of Netnews. ACM Conference on Computer Supported Cooperative Work, 175-186. Chapel Hill, NC.Google Scholar
  86. Rich, E. (1979). Building and Exploiting User Models. Ph.D. Thesis, Carnegie-Mellon University, Pittsburgh, PA.Google Scholar
  87. Rich, E. (1983). Users are Individuals: Individualizing User Models. International Journal of Man-Machine Studies 18: 199–214.Google Scholar
  88. Rich, E. (1989). Stereotypes and User Modeling. In: Kobsa, A. & Wahlster, W. (eds.) User Models in Dialog Systems, 35–51. Berlin, Heidelberg, Springer.Google Scholar
  89. Sarini, M. & Strapparava, C. (1998). Building a User Model for a Museum Exploration and Information-Providing Adaptive System. Proceedings of the 2nd Workshop on Adaptive Hypertext and Hypermedia at HYPERTEXT'98. Pittsburgh, PA. http://ecate.itc. it:1024/strappa/hips-um-ad/hips-um-ad.html.Google Scholar
  90. Sarwar, B. M., Karypis, G., Konstan, J. A. & Riedl, J. (2000). Analysis of Recommender Algorithms for E-Commerce. ACME-Commerce 2000 Conference, 158-167. Minneapolis, MN.Google Scholar
  91. Schreck, J. (2001). Security and Privacy in User Models. Ph.D. Thesis, Dept. of Mathematics and Computer Science, University of Essen, Germany. http://www.ics.uci.edu/ ~kobsa/phds/schreck.pdf. Revised version to appear with Kluwer Academic Publishers, Dordrecht, Netherlands.Google Scholar
  92. Schwab, I & Kobsa, A. (2002). Adaptivity through Unobstrusive Learning. K1 4/02. Special Issue on Adaptivity and User Modeling (to appear).Google Scholar
  93. Schwab, I., Kobsa, A. & Koychev, I. (2000). Learning about Users from Observation. Adaptive User Interfaces: Papers from the 2000 AAAI Spring Symposium, 102–106. Stanford, CA. AAAI Press. http://www.ics.uci.edu/~kobsa/papers/2000-AAAI-kobsa.pdf.Google Scholar
  94. Schwab, I. & Pohl, W. (1999). Learning Information Interest from Positive Examples. UM99 Workshop on Machine Learning for User Modeling. Banff, Canada.Google Scholar
  95. Shardanand, U. & Maes, P. (1995). Social Information Filtering: Algorithms for Automating 'Word of Mouth'. Proceedings of CHI-95, 210-217. Denver, CO.Google Scholar
  96. Shukla, S. & Deshpande, A. (2000). LDAP Directory Services - Just Another Database Application? In: Weidong, C., Naughton, J. & Bernstein, P. (eds.) Proceedings of the 2000 ACMSIGMOD International Conference on Management of Data. NewYork, N.Y: ACM.Google Scholar
  97. Smith, R. (1997). Internet Cryptography. Reading, MA, Addison-Wesley.Google Scholar
  98. Sparck Jones, K. (1972). A Statistical Interpretation of Term Specificity and its Application to Retrieval. Journal of Documentation 28: 11–21.Google Scholar
  99. Specht, M. & Oppermann, R. (1999). User Modeling and Adaptivity in Nomadic Information Systems. 7th GI-Workshop Adaptivität und Benutzermodellierung in Interaktiven Softwaresystemen, 325-328. Magdeburg, Germany. http://fit.gmd.de/~oppi/publications/ABIS-hip.pdf.Google Scholar
  100. Stokes, E., Byrne, D., Blakley, B. & Behera, P. (2000). Access Control Requirements for LDAP. RFC 2820, IETF. http://www.ietf.org/rfc/rfc2820.txt?number=2820.Google Scholar
  101. Wahlster, W. & Kobsa, A. (1989). User Models in Dialog Systems. In: Kobsa, A. & Wahlster, W. (eds.) User Models in Dialog Systems. Heidelberg - Berlin, Springer Verlag.Google Scholar
  102. WebGuide (2001). WebGuide: A City Guide for the Internet, European Media Lab. http://www.eml.org/english/research/deepmap/deepgis/webguide.html.Google Scholar
  103. Woods, E. & Kyral, E. (1997). Ovum Evaluates: Data Mining. Ovum, London, England.Google Scholar

Copyright information

© Kluwer Academic Publishers 2002

Authors and Affiliations

  • Josef Fink
  • Alfred Kobsa

There are no affiliations available

Personalised recommendations