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Developing Constraint-based Recommenders

  • Alexander Felfernig
  • Gerhard Friedrich
  • Dietmar Jannach
  • Markus Zanker
Chapter

Abstract

Traditional recommendation approaches (content-based filtering [48] and collaborative filtering[40]) are well-suited for the recommendation of quality&taste products such as books, movies, or news. However, especially in the context of products such as cars, computers, appartments, or financial services those approaches are not the best choice (see also Chapter 11). For example, apartments are not bought very frequently which makes it rather infeasible to collect numerous ratings for one specific item (exactly such ratings are required by collaborative recommendation algorithms). Furthermore, users of recommender applications would not be satisfied with recommendations based on years-old item preferences (exactly such preferences would be exploited in this context by content-based filtering algorithms).

Keywords

Recommender System Constraint Satisfaction Problem Customer Requirement Conjunctive Query Preference Elicitation 
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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References

  1. 1.
    Bistarelli, S., Montanary, U., Rossi, F.: Semiring-based Constraint Satisfaction and Optimization. Journal of the ACM 44, 201–236 (1997)CrossRefMathSciNetMATHGoogle Scholar
  2. 2.
    Bridge, D.: Towards Conversational Recommender Systems: a Dialogue Grammar Approach. In: D.W. Aha (ed.) Proceedings of the EWCBR-02 Workshop on Mixed Initiative CBR, pp. 9–22 (2002)Google Scholar
  3. 3.
    Bridge, D., Goeker, M., McGinty, L., Smyth, B.: Case-based recommender systems. Knowledge Engineering Review 20(3), 315–320 (2005)CrossRefGoogle Scholar
  4. 4.
    Burke, R.: Knowledge-Based Recommender Systems. Encyclopedia of Library and Information Science 69(32), 180–200 (2000)Google Scholar
  5. 5.
    Burke, R.: Hybrid Recommender Systems: Survey and Experiments. User Modeling and User-Adapted Interaction 12(4), 331–370 (2002)CrossRefMATHGoogle Scholar
  6. 6.
    Burke, R., Hammond, K., Young, B.: Knowledge-based navigation of complex information spaces. In: Proceedings of the 13th National Conference on Artificial Intelligence, AAAI’96, pp. 462–468. AAAI Press (1996)Google Scholar
  7. 7.
    Burke, R., Hammond, K., Young, B.: The FindMe Approach to Assisted Browsing. IEEE Intelligent Systems 12(4), 32–40 (1997)Google Scholar
  8. 8.
    Burnett, M.: HCI research regarding end-user requirement specification: a tutorial. Knowledge-based Systems 16, 341–349 (2003)CrossRefGoogle Scholar
  9. 9.
    Chen, L., Pu, P.: Evaluating Critiquing-based Recommender Agents. In: Proceedings of the 21st National Conference on Artificial Intelligence and the Eighteenth Innovative Ap6 Developing Constraint-based Recommenders 213 plications of Artificial Intelligence Conference, AAAI/IAAI’06, pp. 157–162. AAAI Press, Boston, Massachusetts, USA (2006)Google Scholar
  10. 10.
    Felfernig, A.: Reducing Development and Maintenance Efforts forWeb-based Recommender Applications. Web Engineering and Technology 3(3), 329–351 (2007)CrossRefGoogle Scholar
  11. 11.
    Felfernig, A., Burke, R.: Constraint-based recommender systems: technologies and research issues. In: Proceedings of the 10th International Conference on Electronic Commerce, ICEC’08, pp. 1–10. ACM, New York, NY, USA (2008)CrossRefGoogle Scholar
  12. 12.
    Felfernig, A., Friedrich, G., Jannach, D., Stumptner, M.: Consistency-based diagnosis of configuration knowledge bases. Artificial Intelligence 152(2), 213–234 (2004)CrossRefMathSciNetMATHGoogle Scholar
  13. 13.
    Felfernig, A., Friedrich, G., Jannach, D., Zanker, M.: An integrated environment for the development of knowledge-based recommender applications. International Journal of Electronic Commerce 11(2), 11–34 (2007)CrossRefGoogle Scholar
  14. 14.
    Felfernig, A., Friedrich, G., Schubert, M., Mandl, M., Mairitsch, M., Teppan, E.: Plausible Repairs for Inconsistent Requirements. In: Proceedings of the 21st International Joint Conference on Artificial Intelligence, IJCAI’09, pp. 791–796. Pasadena, CA, USA (2009)Google Scholar
  15. 15.
    Felfernig, A., Gula, B.: An Empirical Study on Consumer Behavior in the Interaction with Knowledge-based Recommender Applications. In: Proceedings of the 8th IEEE International Conference on E-Commerce Technology (CEC 2006) / Third IEEE International Conference on Enterprise Computing, E-Commerce and E-Services (EEE 2006), p. 37 (2006)Google Scholar
  16. 16.
    Felfernig, A., Isak, K., Kruggel, T.: Testing Knowledge-based Recommender Systems. OEGAI Journal 4, 12–18 (2007)Google Scholar
  17. 17.
    Felfernig, A., Isak, K., Szabo, K., Zachar, P.: The VITA Financial Services Sales Support Environment. In: Proceedings of the 22nd AAAI Conference on Artificial Intelligence and the 19th Conference on Innovative Applications of Artificial Intelligence, AAAI/IAAI’07, pp. 1692–1699. Vancouver, Canada (2007)Google Scholar
  18. 18.
    Felfernig, A., Kiener, A.: Knowledge-based Interactive Selling of Financial Services using FSAdvisor. In: Proceedings of the 20th National Conference on Artificial Intelligence and the 17th Innovative Applications of Artificial Intelligence Conference, AAAI/IAAI’05, pp.1475–1482. AAAI Press, Pittsburgh, PA (2005)Google Scholar
  19. 19.
    Felfernig, A., Mairitsch, M., Mandl, M., Schubert, M., Teppan, E.: Utility-based Repair of Inconsistent Requirements. In: Proceedings of the 22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligence Systems, IEAAIE 2009, Springer Lecture Notes on Artificial Intelligence, pp. 162–171. Springer, Taiwan (2009)Google Scholar
  20. 20.
    Felfernig, A., Shchekotykhin, K.: Debugging user interface descriptions of knowledge-based recommender applications. In: Proceedings of the 11th International Conference on Intelligent User Interfaces, IUI 2006, pp. 234–241. ACM Press, New York, NY, USA (2006)CrossRefGoogle Scholar
  21. 21.
    Felfernig, A., Teppan, E., Friedrich, G., Isak, K.: Intelligent debugging and repair of utility constraint sets in knowledge-based recommender applications. In: Proceedings of the ACM International Conference on Intelligent User Interfaces, IUI 2008, pp. 217–226 (2008)Google Scholar
  22. 22.
    Gil, Y., Motta, E., Benjamins, V., Musen, M. (eds.): The Semantic Web - ISWC 2005, 4th International SemanticWeb Conference, ISWC 2005, Galway, Ireland, November 6-10, 2005, Lecture Notes in Computer Science, vol. 3729. Springer (2005)Google Scholar
  23. 23.
    Godfrey, P.: Minimization in Cooperative Response to Failing Database Queries. International Journal of Cooperative Information Systems 6(2), 95–149 (1997)CrossRefGoogle Scholar
  24. 24.
    Herlocker, J., Konstan, J., Terveen, L., Riedl, J.: Evaluating Collaborative Filtering Recommender Systems. ACM Transactions on Information Systems 22(1), 5–53 (2004)CrossRefGoogle Scholar
  25. 25.
    Jannach, D.: Advisor Suite - A knowledge-based sales advisory system. In: R.L. de Mantaras, L. Saitta (eds.) Proceedings of European Conference on Artificial Intelligence, ECAI 2004, pp. 720–724. IOS Press, Valencia, Spain (2004)Google Scholar
  26. 26.
    Jannach, D.: Techniques for Fast Query Relaxation in Content-based Recommender Systems. In: C. Freksa, M. Kohlhase, K. Schill (eds.) Proceedings of the 29th German Conference on AI, KI 2006, pp. 49–63. Springer LNAI 4314, Bremen, Germany (2006)Google Scholar
  27. 27.
    Jannach, D.: Fast computation of query relaxations for knowledge-based recommenders. AI Communications 22(4), 235–248 (2009)MATHGoogle Scholar
  28. 28.
    Jannach, D., Bundgaard-Joergensen, U.: SAT: AWeb-Based Interactive Advisor For Investor-Ready Business Plans. In: Proceedings of International Conference on e-Business, pp. 99–106 (2007)Google Scholar
  29. 29.
    Jannach, D., Kreutler, G.: Personalized User Preference Elicitation for e-Services. In: Proceedings of the IEEE International Conference on e-Technology, e-Commerce, and e- Services, EEE 2005, pp. 604–611. IEEE Computer Society, Hong Kong (2005)CrossRefGoogle Scholar
  30. 30.
    Jannach, D., Kreutler, G.: Rapid Development Of Knowledge-Based Conversational Recommender Applications With Advisor Suite. Journal of Web Engineering 6, 165–192 (2007)Google Scholar
  31. 31.
    Jannach, D., Shchekotykhin, K., Friedrich, G.: Automated Ontology Instantiation from Tabular Web Sources - The AllRight System. Journal of Web Semantics 7(3), 136–153 (2009)Google Scholar
  32. 32.
    Jannach, D., Zanker, M., Fuchs, M.: Constraint-based recommendation in tourism: A multiperspective case study. Information Technology and Tourism 11(2), 139–156 (2009)CrossRefGoogle Scholar
  33. 33.
    Junker, U.: QUICKXPLAIN: Preferred Explanations and Relaxations for Over-Constrained Problems. In: Proceedings of National Conference on Artificial Intelligence, AAAI’04, pp. 167–172. AAAI Press, San Jose (2004)Google Scholar
  34. 34.
    Konstan, J., Miller, N., Maltz, D., Herlocker, J., Gordon, R., Riedl, J.: GroupLens: applying collaborative filtering to Usenet news. Communications of the ACM 40(3), 77–87 (1997)CrossRefGoogle Scholar
  35. 35.
    Lakshmanan, L., Leone, N., Ross, R., Subrahmanian, V.: ProbView: A Flexible Probabilistic Database System. ACM Transactions on Database Systems 22(3), 419–469 (1997)CrossRefGoogle Scholar
  36. 36.
    Lorenzi, F., Ricci, F., Tostes, R., Brasil, R.: Case-based recommender systems: A unifying view. In: Intelligent Techniques in Web Personalisation, no. 3169 in Lecture Notes in Computer Science, pp. 89–113. Springer (2005)Google Scholar
  37. 37.
    Mahmood, T., Ricci, F.: Learning and adaptivity in interactive recommender systems. In: Proceedings of the 9th International Conference on Electronic Commerce, ICEC’07, pp. 75–84. ACM Press, New York, NY, USA (2007)Google Scholar
  38. 38.
    Maimon, O., Rokach, L. Data Mining by Attribute Decomposition with semiconductors manufacturing case study, in Data Mining for Design and Manufacturing: Methods and Applications, D. Braha (ed.), Kluwer Academic Publishers, pp. 311–336 (2001)Google Scholar
  39. 39.
    McSherry, D.: Incremental Relaxation of Unsuccessful Queries. In: P. Funk, P.G. Calero (eds.) Proceedings of the European Conference on Case-based Reasoning, ECCBR 2004, no. 3155 in Lecture Notes in Artificial Intelligence, pp. 331–345. Springer (2004)Google Scholar
  40. 40.
    McSherry, D.: Retrieval Failure and Recovery in Recommender Systems. Artificial Intelligence Review 24(3-4), 319–338 (2005)CrossRefGoogle Scholar
  41. 41.
    Mirzadeh, N., Ricci, F., Bansal, M.: Feature Selection Methods for Conversational Recommender Systems. In: Proceedings of the 2005 IEEE International Conference on e- Technology, e-Commerce and e-Service on e-Technology, e-Commerce and e-Service, EEE 2005, pp. 772–777. IEEE Computer Society, Washington, DC, USA (2005)Google Scholar
  42. 42.
    Pazzani, M.: A Framework for Collaborative, Content-Based and Demographic Filtering. Artificial Intelligence Review 13(5-6), 393–408 (1999)CrossRefGoogle Scholar
  43. 43.
    Peischl, B., Nica, M., Zanker, M., Schmid, W.: Recommending effort estimation methods for software project management. In: Proceedings of the International Conference on Web Intelligence and Intelligent Agent Technology - WPRRS Workshop, vol. 3, pp. 77–80. Milano, Italy (2009)Google Scholar
  44. 44.
    Reilly, J., McCarthy, K., McGinty, L., Smyth, B.: Dynamic Critiquing. In: Proceedings of the 7th European Conference on Case-based Reasoning, ECCBR 2004, pp. 763–777. Madrid, Spain (2004)Google Scholar
  45. 45.
    Reiter, R.: A theory of diagnosis from first principles. Artificial Intelligence 32(1), 57–95 (1987)CrossRefMathSciNetMATHGoogle Scholar
  46. 46.
    R.Elmasri, Navathe, S.: Fundamentals of Database Systems. Addison Wesley (2006)Google Scholar
  47. 47.
    Ricci, F., Mirzadeh, N., Bansal, M.: Supporting User Query Relaxation in a Recommender System. In: Proceedings of the 5th International Conference in E-Commerce and Web-Technologies, EC-Web 2004, pp. 31–40. Zaragoza, Spain (2004)Google Scholar
  48. 48.
    Ricci, F., Mirzadeh, N., Venturini, A.: Intelligent query management in a mediator architecture. In: Proceedings of the 1st International IEEE Symposium on Intelligent Systems, vol. 1, pp. 221–226. Varna, Bulgaria (2002)Google Scholar
  49. 49.
    Ricci, F., Nguyen, Q.: Acquiring and Revising Preferences in a Critique-Based Mobile Recommender System. IEEE Intelligent Systems 22(3), 22–29 (2007)CrossRefGoogle Scholar
  50. 50.
    Ricci, F., Venturini, A., Cavada, D., Mirzadeh, N., Blaas, D., Nones, M.: Product Recommendation with Interactive Query Management and Twofold Similarity. In: Proceedings of the 5th International Conference on Case-Based Reasoning, pp. 479–493. Trondheim, Norway (2003)Google Scholar
  51. 51.
    Shchekotykhin, K., Friedrich, G.: Argumentation based constraint acquisition. In: Proceedings of the IEEE International Conference on Data Mining (2009)Google Scholar
  52. 52.
    Smyth, B., McGinty, L., Reilly, J., McCarthy, K.: Compound Critiques for Conversational Recommender Systems. In: Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence, WI’04, pp. 145–151. Maebashi, Japan (2004)CrossRefGoogle Scholar
  53. 53.
    Thompson, C., Goeker, M., Langley, P.: A Personalized System for Conversational Recommendations. Journal of Artificial Intelligence Research 21, 393–428 (2004)Google Scholar
  54. 54.
    Tsang, E.: Foundations of Constraint Satisfaction. Academic Press, London and San Diego (1993)Google Scholar
  55. 55.
    Williams, M., Tou, F.: RABBIT: An interface for database access. In: Proceedings of the ACM ’82 Conference, ACM’82, pp. 83–87. ACM, New York, NY, USA (1982)CrossRefGoogle Scholar
  56. 56.
    Winterfeldt, D., Edwards, W.: Decision Analysis and Behavioral Research. Cambridge University Press (1986)Google Scholar
  57. 57.
    Zanker, M.: A Collaborative Constraint-Based Meta-Level Recommender. In: Proceedings of the 2nd ACM International Conference on Recommender Systems, RecSys 2008, pp. 139–146. ACM Press, Lausanne, Switzerland (2008)CrossRefGoogle Scholar
  58. 58.
    Zanker, M., Bricman, M., Gordea, S., Jannach, D., Jessenitschnig, M.: Persuasive onlineselling in quality & taste domains. In: Proceedings of the 7th International Conference on Electronic Commerce and Web Technologies, EC-Web 2006, pp. 51–60. Springer, Krakow, Poland (2006)Google Scholar
  59. 59.
    Zanker, M., Fuchs, M., Höpken,W., Tuta, M., Müller, N.: Evaluating Recommender Systems in Tourism - A Case Study from Austria. In: Proceedings of the International Conference on Information and Communication Technologies in Tourism, ENTER 2008, pp. 24–34 (2008)Google Scholar
  60. 60.
    Zanker, M., Jessenitschnig, M.: Case-studies on exploiting explicit customer requirements in recommender systems. User Modeling and User-Adapted Interaction: The Journal of Personalization Research, A. Tuzhilin and B. Mobasher (Eds.): Special issue on Data Mining for Personalization 19(1-2), 133–166 (2009)Google Scholar
  61. 61.
    Zanker, M., Jessenitschnig, M., Jannach, D., Gordea, S.: Comparing recommendation strategies in a commercial context. IEEE Intelligent Systems 22(May/Jun), 69–73 (2007)Google Scholar
  62. 62.
    Zhang, J., Jones, N., Pu, P.: A visual interface for critiquing-based recommender systems. In: Proceedings of the 9th ACM Conference on Electronic Commerce, EC’08, pp. 230–239. ACM, New York, NY, USA (2008)CrossRefGoogle Scholar
  63. 63.
    Ziegler, C.: Semantic Web Recommender Systems. In: Proceedings of the EDBT Workshop, EDBT’04, pp. 78–89 (2004)Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Alexander Felfernig
    • 1
  • Gerhard Friedrich
    • 2
  • Dietmar Jannach
    • 3
  • Markus Zanker
    • 2
  1. 1.Graz University of TechnologyGrazAustria
  2. 2.University KlagenfurtKlagenfurtAustria
  3. 3.TU DortmundDortmundGermany

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