Advertisement

HYREC: A Hybrid Recommendation System for E-Commerce

  • Bhanu Prasad
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3620)

Abstract

Product recommendation is very important in business to customer (B2C) e-commerce. Automated Collaborative Filtering (ACF) is an important approach for product recommendation. However, a major drawback with this approach is that it can’t avoid the “sequence recognition problem”, explained in this paper. Here we present a system that addresses the sequence recognition problem by recording and utilizing the users’ purchase patterns and ratings. The proposed system is a fruitful combination of ACF and Case-Based Reasoning Plan Recognition (CBRPR) methods. The evaluation studies prove that the hybrid system provides better performance when compared to ACF and CBRPR methods used individually.

Keywords

Recommendation System Acceptance Rate Collaborative Filter Product Recommendation Recommendation Process 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Albrecht, D.W., Zukerman, I., Nicholson, A., Bud, A.: Towards a Bayesian Model for Keyhole Plan Recognition in Large Domains. In: Proceedings of the 6th International Conference on User Modelling, pp. 365–376 (1997)Google Scholar
  2. 2.
    Allen, J.F., Perrault, C.R.: Analyzing Intention in Dialogues. Artificial Intelligence 15(3), 143–178 (1980)CrossRefGoogle Scholar
  3. 3.
    Balabanovic, M., Shoham, Y.: Fab: Content-based, Collaborative Recommendation. Communications of the ACM 40(3) (March 1997)Google Scholar
  4. 4.
    Bauer, M.: Acquisition of User Preferences for Plan Recognition. In: Proceedings of the 5th International Conference on User Modelling, pp. 936–941 (1998)Google Scholar
  5. 5.
    Bergmann, R., Richter, M.M., Schmitt, S., Stahl, A., Vollrath, I.: Utilityoriented Matching: A New Research Direction for Case-Based Reasoning. Professionelles Wissensmanagement: Erfahrungen und Visionen. In: Proceedings of the 1st Conference on Professional Knowledge Management. Shaker (2001)Google Scholar
  6. 6.
    Bergmann, R., Schmitt, S., Stahl, A.: Intelligent Customer Support for Product Selection with Case-based Reasoning. In: E-commerce and Intelligent Methods, pp. 322–341. Physica-Verlag, New york (2002)Google Scholar
  7. 7.
    Branting, K.L.: Learning Feature Weights from Customer Return-Set Selections. Journal of Knowledge and Information Systems 6(2) (2004)Google Scholar
  8. 8.
    Bui, H.H.: Efficient Approximate Inference for Online Probabilistic Plan Recognition. Technical Report 1/2002, School of Computing, Curtin University of Technology, Perth, WA, Australia (2002)Google Scholar
  9. 9.
    Burke, R.: Integrating Knowledge-Based and Collaborative-Filtering Recommender Systems. In: Proceedings of the AAAI 1999 Workshop on AI for Electronic Commerce (1998)Google Scholar
  10. 10.
    Charniak, E., Goldman, R.: A Bayesian Model of Plan Recognition. Artificial Intelligence Journal 64, 53–79 (1993)CrossRefGoogle Scholar
  11. 11.
    Cohen, R., Song, F., Spencer, B., van Beek, P.: Exploiting Temporal and Novel Information from the User in Plan Recognition. User Modelling and User-Adapted Interaction 1(2), 125–148 (1981)Google Scholar
  12. 12.
    Cotter, P., Smyth, B.: PTV: Intelligent Personalised TV Guides. In: Proceedings of the 12th Innovative Applications of Artificial Intelligence (IAAI 2000) Conference. AAAI Press, Menlo Park (2000)Google Scholar
  13. 13.
    Cunningham, P.: Intelligent Support for E-commerce. Keynote speech slides presented at the International Conference on Case-Based Reasoning, ICCBR 1999 (1999), Also available at http://www.cs.tcd.ie/Padraig.Cunningham/iccbr99-ec.pdf (accessed on December 26, 2004)
  14. 14.
    Cunningham, P., Bergmann, R., Schmitt, S., Breen, S., Smyth, B., Traphoener, R.: Intelligent Support for Online Sales: The Websell Experience (2001), http://www.aic.nrl.navy.mil/papers/2001/AIC-01-003/ws3/ws3toc6.pdf (accessed on December 26, 2004)
  15. 15.
    Ferguson, G., Allen, J.F.: Events and Actions in the Interval Temporal Logic. Journal of Logic and Computation, Special Issue on Actions and Processes 4(5), 531–579 (1994)zbMATHMathSciNetGoogle Scholar
  16. 16.
    Gronau, N., Kreymborg, C., Laskowski, F.: Improving Information Retrieval in Knowledge Management Systems using CBR - The Multi Reuse Approach of the Project TO_KNOW. In: Proceedings of the 1st Indian International Conference on Artificial Intelligence, Hyderabad, India, pp. 779–788 (2003)Google Scholar
  17. 17.
    Hammond, K., Schmitt, K.: A Case-Based Approach to Knowledge Navigation. In: Proceedings of the AAAI Workshop on Indexing and Reuse in Multimedia Systems. AAAI Press, Menlo Park (1994)Google Scholar
  18. 18.
    Hayes, C., Cunningham, P.: Shaping a CBR view with XML. In: Proceedings of the 3rd International Conference on Case-based Reasoning, pp. 468–481 (2000)Google Scholar
  19. 19.
    Hayes, C., Cunningham, P.: Context Boosting Collaborative Recommendations. Knowledge-Based Systems 17(2-4), 131–138 (2003)CrossRefGoogle Scholar
  20. 20.
    Hayes, C., Cunningham, P., Smyth, B.: A Case-based Reasoning View of Automated Collaborative Filtering. In: Aha, D.W., Watson, I. (eds.) ICCBR 2001. LNCS (LNAI), vol. 2080, pp. 234–248. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  21. 21.
    Hill, W., Stead, L., Rosenstein, M., Furnas, G.: Recommending and Evaluating Choices in a Virtual Community of Use. In: Proceedings of Conference on Human Factors in Computing Systems (1995)Google Scholar
  22. 22.
    Kautz, H.: A Formal Theory of Plan Recognition and its Implementation. In: Allen, J., Pelavin, R., Tenenberg, J. (eds.) Reasoning About Plans, pp. 69–125. Morgan Kaufmann, San Mateo (1991)Google Scholar
  23. 23.
    Kerkez, B., Cox, M.: Incremental Case-Based Plan Recognition Using State Indices. In: Proceedings of 4th International Conference on Case-Based Reasoning, pp. 291–305 (2001)Google Scholar
  24. 24.
    Kohlmaier, A., Schmitt, S., Bergmann, R.: A Similarity-based Approach to Attribute Selection in User Adaptive Sales Dialogs. In: Aha, D.W., Watson, I. (eds.) ICCBR 2001. LNCS (LNAI), vol. 2080, pp. 306–320. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  25. 25.
    Konstan, J., Miller, B., Maltz, D., Herlocker, J., Gordon, L., Riedl, J.: GroupLens: Applying Collaborative Filtering to Usenet News. Communications of the ACM 40(3), 77–87 (1997)CrossRefGoogle Scholar
  26. 26.
    Kowalczyk, R., Pham, A., Rahwan, D.: Intelligent Agents for One-to-Many Automated E-Commerce Negotiation. In: Proceedings of the Australasian Computer Science Conference, Australia (2002)Google Scholar
  27. 27.
    Lesh, N., Rich, C., Sidner, C.: Using Plan Recognition in Human-Computer Collaboration. In: Proceedings of the 7th International Conference on User Modelling, pp. 23–32 (1999)Google Scholar
  28. 28.
    Pazzani, M.J.: Beyond Idiot Savants: Recommendations and Common Sense. In: Beyond Personalization 2005: A Workshop on the Next Stage of Recommender Systems Research, held in conjunction with the 2005 International Conference on Intelligent User Interfaces (IUI 2005), San Diego, California, USA (2005), The paper is also available at http://www.grouplens.org/beyond2005/position/pazzani.pdf (accessed on March 18 2005)
  29. 29.
    Perry, P.: Resources on Collaborative Filtering, http://www.paulperry.net/notes/cf.asp (accessed on December 26, 2004)
  30. 30.
    Prasad, B.: Learning the Users’ Preferences in E-Commerce: A Weight-adjustment Approach. International Journal of Knowledge-Based and Intelligent Engineering Systems 8(4), 205–211 (2004)Google Scholar
  31. 31.
    Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: GroupLens: An Open Architecture for Collaborative Filtering of Netnews. In: Proceedings of the ACM 1994 Conference on Computer Supported Cooperative Work (CSCW 1994), Chapel Hill, NC, USA (1994)Google Scholar
  32. 32.
    Resnick, P., Varian, H.R.: Recommender Systems. Special issue of Communications of the ACM 40(3) (1997)Google Scholar
  33. 33.
    Sarwar, B.M., Karypis, G., Konstan, J.A., Riedl, J.: Item-based Collaborative Filtering Recommendation Algorithms. In: Proceedings of the 10th International World Wide Web Conference (WWW10), Hong Kong (2001)Google Scholar
  34. 34.
    Shardanand, U., Maes, P.: Social Information Filtering: Algorithms for Automating “Word of Mouth”. In: Conference on Human Factors in Computing Systems (1995)Google Scholar
  35. 35.
    Sollenborn, M., Funk, P.: Category-Based Filtering and User Stereotype Cases to Reduce the Latency Problem in Recommender Systems. In: Craw, S., Preece, A.D. (eds.) ECCBR 2002. LNCS (LNAI), vol. 2416, pp. 395–405. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  36. 36.
    Stahl, A.: Learning Feature Weights from Case Order Feedback. In: Aha, D.W., Watson, I. (eds.) ICCBR 2001. LNCS (LNAI), vol. 2080, pp. 502–516. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  37. 37.
    Tran, T., Cohen, R.: Hybrid Recommender Systems for Electronic Commerce. In: Proceedings of the AAAI 2000 Workshop on Knowledge-Based Electronic Markets, USA (1999)Google Scholar
  38. 38.
    Vollrath, I., Wilke, W., Bergmann, R.: Case-Based Reasoning Support for Online Catalog Sales. IEEE Internet Computing 2(4), 45–54 (1998)CrossRefGoogle Scholar
  39. 39.
    Watson, I.: Applying Case-Based Reasoning: Techniques for Enterprise Systems. Morgan Kaufmann Publishers, San Francisco (1997)zbMATHGoogle Scholar
  40. 40.
    Wettschereck, D., Aha, D.W.: Weighting Features. In: Proceedings of the 1st International Conference on Case-Based Reasoning. Springer, New York (1995)Google Scholar
  41. 41.
    Wilke, W.: Knowledge Management for Intelligent Sales Support in Electronic Commerce. Ph.D. Dissertation, University of Kaiserslautern, Germany (1999)Google Scholar
  42. 42.
    Yang, Q., Li, I.T.Y., Zhang, H.H.: Mining High-Quality Cases for Hypertext Prediction and Prefetching. In: Aha, D.W., Watson, I. (eds.) ICCBR 2001. LNCS (LNAI), vol. 2080, p. 744. Springer, Heidelberg (2001)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Bhanu Prasad
    • 1
  1. 1.Department of Computer and Information SciencesFlorida A&M UniversityTallahasseeUSA

Personalised recommendations