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Completeness Criteria for Retrieval in Recommender Systems

  • David McSherry
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4106)

Abstract

Often in practice, a recommender system query may include constraints that must be satisfied. Ensuring the retrieval of a product that satisfies any hard constraints in a given query, if such a product exists, is one benefit of a retrieval criterion we refer to as completeness. Other benefits include the ease with which thenon-existence of an acceptable product can often be recognized from the results for a given query, and the ability to justify the exclusion of any product from the retrieval set on the basis that one of the retrieved products satisfies at least the same constraints. We show that in contrast to most retrieval strategies, compromise driven retrieval (CDR) is complete. Another important benefit of CDR is its ability to ensure the retrieval of the most similar product, if any, which satisfies all the hard constraints in a given query, a criterion we refer to as optimal completeness.

Keywords

Case Base Recommender System Soft Constraint Hard Constraint Retrieval Strategy 
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.
    Wilke, W., Lenz, M., Wess, S.: Intelligent Sales Support with CBR. In: Lenz, M., Bartsch-Spörl, B., Burkhard, H.-D., Wess, S. (eds.) Case-Based Reasoning Technology, pp. 91–113. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  2. 2.
    McSherry, D.: Similarity and Compromise. In: Ashley, K.D., Bridge, D.G. (eds.) ICCBR 2003. LNCS, vol. 2689, pp. 291–305. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  3. 3.
    McSherry, D.: On the Role of Default Preferences in Compromise-Driven Retrieval. In: Proceedings of the 10th UK Workshop on Case-Based Reasoning, pp. 11–19 (2005)Google Scholar
  4. 4.
    McSherry, D.: Coverage-Optimized Retrieval. In: Proceedings of the 18th International Joint Conference on Artificial Intelligence, pp. 1349–1350 (2003)Google Scholar
  5. 5.
    McSherry, D.: Balancing User Satisfaction and Cognitive Load in Coverage-Optimised Retrieval. Knowledge-Based Systems 17, 113–119 (2004)CrossRefGoogle Scholar
  6. 6.
    Bridge, D., Ferguson, A.: Diverse Product Recommendations using an Expressive Language for Case Retrieval. In: Craw, S., Preece, A.D. (eds.) ECCBR 2002. LNCS (LNAI), vol. 2416, pp. 43–57. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  7. 7.
    McSherry, D.: Diversity-Conscious Retrieval. In: Craw, S., Preece, A.D. (eds.) ECCBR 2002. LNCS (LNAI), vol. 2416, pp. 219–233. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  8. 8.
    Smyth, B., McClave, P.: Similarity vs. Diversity. In: Aha, D.W., Watson, I. (eds.) ICCBR 2001. LNCS, vol. 2080, pp. 347–361. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  9. 9.
    Branting, L.K.: Acquiring Customer Preferences from Return-Set Selections. In: Aha, D.W., Watson, I. (eds.) ICCBR 2001. LNCS (LNAI), vol. 2080, pp. 59–73. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  10. 10.
    Bridge, D., Ferguson, A.: An Expressive Query Language for Product Recommender Systems. Artificial Intelligence Review 18, 269–307 (2002)CrossRefGoogle Scholar
  11. 11.
    Burkhard, H.-D.: Extending Some Concepts of CBR - Foundations of Case Retrieval Nets. In: Lenz, M., Bartsch-Spörl, B., Burkhard, H.-D., Wess, S. (eds.) Case-Based Reasoning Technology, pp. 17–50. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  12. 12.
    Ferguson, A., Bridge, D.: Partial Orders and Indifference Relations: Being Purposefully Vague in Case-Based Retrieval. In: Blanzieri, E., Portinale, L. (eds.) EWCBR 2000. LNCS (LNAI), vol. 1898, pp. 74–85. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  13. 13.
    McSherry, D.: A Generalised Approach to Similarity-Based Retrieval in Recommender Systems. Artificial Intelligence Review 18, 309–341 (2002)CrossRefGoogle Scholar
  14. 14.
    McSherry, D.: Recommendation Engineering. In: Proceedings of the 15th European Conference on Artificial Intelligence, pp. 86–90. IOS Press, Amsterdam (2002)Google Scholar
  15. 15.
    McSherry, D.: The Inseparability Problem in Interactive Case-Based Reasoning. Knowledge-Based Systems 15, 293–300 (2002)CrossRefGoogle Scholar
  16. 16.
    McSherry, D., Stretch, C.: Automating the Discovery of Recommendation Knowledge. In: Proceedings of the 19th International Joint Conference on Artificial Intelligence, pp. 9–14 (2005)Google Scholar
  17. 17.
    Kießling, W.: Foundations of Preferences in Database Systems. In: Proceedings of the 28th International Conference on Very Large Databases, pp. 311–322 (2002)Google Scholar
  18. 18.
    Balke, W.-T., Günzer, U.: Efficient Skyline Queries under Weak Pareto Dominance. In: IJCAI 2005 Workshop on Advances in Preference Handling, pp. 1–6 (2005)Google Scholar
  19. 19.
    Hong, I., Vogel, D.: Data and Model Management in a Generalised MCDM-DSS. Decision Sciences 22, 1–25 (1991)CrossRefGoogle Scholar
  20. 20.
    Linden, G., Hanks, S., Lesh, N.: Interactive Assessment of User Preference Models: The Automated Travel Assistant. In: Proceedings of the 6th International Conference on User Modeling, pp. 67–78 (1997)Google Scholar
  21. 21.
    Aha, D.W., Breslow, L.A., Muñoz-Avila, H.: Conversational Case-Based Reasoning. Applied Intelligence 14, 9–32 (2001)MATHCrossRefGoogle Scholar
  22. 22.
    Doyle, M., Cunningham, P.: A Dynamic Approach to Reducing Dialog in On-Line Decision Guides. In: Blanzieri, E., Portinale, L. (eds.) EWCBR 2000. LNCS (LNAI), vol. 1898, pp. 49–60. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  23. 23.
    Kohlmaier, A., Schmitt, S., Bergmann, R.: A Similarity-Based Approach to Attribute Selection in User-Adaptive Sales Dialogues. In: Aha, D.W., Watson, I. (eds.) ICCBR 2001. LNCS (LNAI), vol. 2080, pp. 306–320. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  24. 24.
    McSherry, D.: Minimizing Dialog Length in Interactive Case-Based Reasoning. In: Proceedings of the 17th International Joint Conference on Artificial Intelligence, pp. 993–998 (2001)Google Scholar
  25. 25.
    McSherry, D.: Increasing Dialogue Efficiency in Case-Based Reasoning without Loss of Solution Quality. In: Proceedings of the 18th International Joint Conference on Artificial Intelligence, pp. 121–126 (2003)Google Scholar
  26. 26.
    McSherry, D.: Conversational CBR in Multi-Agent Recommendation. In: IJCAI 2005 Workshop on Multi-Agent Information Retrieval and Recommender Systems, pp. 331–345 (2005)Google Scholar
  27. 27.
    McSherry, D.: Explanation in Recommender Systems. Artificial Intelligence Review 24, 179–197 (2005)MATHCrossRefGoogle Scholar
  28. 28.
    McSherry, D.: Incremental Nearest Neighbour with Default Preferences. In: Proceedings of the 16th Irish Conference on Artificial Intelligence and Cognitive Science, pp. 9–18 (2005)Google Scholar
  29. 29.
    Shimazu, H.: ExpertClerk: A Conversational Case-Based Reasoning Tool for Developing Salesclerk Agents in E-Commerce Webshops. Artificial Intelligence Review 18, 223–244 (2002)CrossRefGoogle Scholar
  30. 30.
    Thompson, C.A., Göker, M.H., Langley, P.: A Personalized System for Conversational Recommendations. Journal of Artificial Intelligence Research 21, 393–428 (2004)Google Scholar
  31. 31.
    Burke, R., Hammond, K.J., Young, B.: The FindMe Approach to Assisted Browsing. IEEE Expert 12, 32–40 (1997)CrossRefGoogle Scholar
  32. 32.
    Hammond, K.J., Burke, R., Schmitt, K.: A Case-Based Approach to Knowledge Navigation. In: Leake, D.B. (ed.) Case-Based Reasoning: Experiences, Lessons & Future Directions, pp. 125–136. AAAI Press/MIT Press, Menlo Park (1996)Google Scholar
  33. 33.
    McSherry, D.: Explanation of Retrieval Mismatches in Recommender System Dialogues. In: ICCBR 2003 Workshop on Mixed-Initiative Case-Based Reasoning, pp. 191–199 (2003)Google Scholar
  34. 34.
    McSherry, D.: Incremental Relaxation of Unsuccessful Queries. In: Funk, P., González Calero, P.A. (eds.) ECCBR 2004. LNCS (LNAI), vol. 3155, pp. 331–345. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  35. 35.
    McSherry, D.: Maximally Successful Relaxations of Unsuccessful Queries. In: Proceedings of the 15th Conference on Artificial Intelligence and Cognitive Science, pp. 127–136 (2004)Google Scholar
  36. 36.
    McSherry, D.: Retrieval Failure and Recovery in Recommender Systems. Artificial Intelligence Review 24, 319–338 (2005)CrossRefGoogle Scholar
  37. 37.
    Ricci, F., Arslan, B., Mirzadeh, N., Venturini, A.: ITR: A Case-Based Travel Advisory System. In: Craw, S., Preece, A.D. (eds.) ECCBR 2002. LNCS (LNAI), vol. 2416, pp. 613–627. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  38. 38.
    Gaasterland, T., Godfrey, P., Minker, J.: An Overview of Cooperative Answering. Journal of Intelligent Information Systems 1, 123–157 (1992)CrossRefGoogle Scholar
  39. 39.
    Godfrey, P.: Minimisation in Cooperative Response to Failing Database Queries. International Journal of Cooperative Information Systems 6, 95–149 (1997)CrossRefGoogle Scholar
  40. 40.
    Kaplan, S.J.: Cooperative Responses from a Portable Natural Language Query System. Artificial Intelligence 19, 165–187 (1982)CrossRefGoogle Scholar
  41. 41.
    McCarthy, K., Reilly, J., McGinty, L., Smyth, B.: Experiments in Dynamic Critiquing. In: Proceedings of the 10th International Conference on Intelligent User Interfaces, pp. 175–182 (2005)Google Scholar
  42. 42.
    Lieber, J., Napoli, A.: Correct and Complete Retrieval for Case-Based Problem Solving. In: Proceedings of the 13th European Conference on Artificial Intelligence, pp. 68–72. Wiley, Chichester (1998)Google Scholar
  43. 43.
    Muñoz-Avila, H.: Case-base Maintenance by Integrating Case-Index Revision and Case-Retention Policies in a Derivational Replay Framework. Computational Intelligence 17, 280–294 (2001)CrossRefGoogle Scholar
  44. 44.
    Russell, S., Norvig, S.: Artificial Intelligence: A Modern Approach. Prentice-Hall, Upper Saddle River (1995)MATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • David McSherry
    • 1
  1. 1.School of Computing and Information EngineeringUniversity of UlsterColeraineNorthern Ireland

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