Comparing Two Recommender Algorithms with the Help of Recommendations by Peers

  • Andreas Geyer-Schulz
  • Michael Hahsler
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2703)


Since more and more Web sites, especially sites of retailers, offer automatic recommendation services using Web usage mining, evaluation of recommender algorithms has become increasingly important. In this paper we present a framework for the evaluation of different aspects of recommender systems based on the process of discovering knowledge in databases introduced by Fayyad et al. and we summarize research already done in this area. One aspect identified in the presented evaluation framework is widely neglected when dealing with recommender algorithms. This aspect is to evaluate how useful patterns extracted by recommender algorithms are to support the social process of recommending products to others, a process normally driven by recommendations by peers or experts. To fill this gap for recommender algorithms based on frequent itemsets extracted from usage data we evaluate the usefulness of two algorithms. The first recommender algorithm uses association rules, and the other algorithm is based on the repeat-buying theory known from marketing research. of usage data from an educational Internet information broker and compare useful recommendations identified by users from the target group of the broker (peers) with the recommendations produced by the algorithms. The results of the evaluation presented in this paper suggest that frequent itemsets from usage histories match the concept of useful recommendations expressed by peers with satisfactory accuracy (higher than 70%) and precision (between 60% and 90%). Also the evaluation suggests that both algorithms studied in the paper perform similar on real-world data if they are tuned properly.


Association Rule Recommender System Minimum Support Frequent Itemsets Support Threshold 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P.: From Data Mining to Knowledge Discovery: An Overview, pp. 1–36. MIT Press, Cambridge (1996)Google Scholar
  2. 2.
    Resnick, P., Varian, H.R.: Recommender systems. Communications of the ACM 40, 56–58 (1997)CrossRefGoogle Scholar
  3. 3.
    Shardanand, U., Maes, P.: Social information filtering: Algorithms for automating ’word of mouth’. In: Conference proceedings on Human factors in computing systems (CHI 1995), Denver, CO, pp. 210–217. ACM Press/Addison-Wesley Publishing Co. (1995)Google Scholar
  4. 4.
    Spiliopoulou, M.: Web usage mining for web site evaluation. Communications of the ACM 43, 127–134 (2000)CrossRefGoogle Scholar
  5. 5.
    Mobasher, B., Cooley, R., Srivastava, J.: Automatic personalization based on web usage mining. Communications of the ACM 43, 142–151 (2000)CrossRefGoogle Scholar
  6. 6.
    Wirth, R., Hipp, J.: CRISP-DM: Towards a standard process modell for data mining. In: Proceedings of the 4th International Conference on the Practical Applications of Knowledge Discovery and Data Mining, Manchester, UK (2000)Google Scholar
  7. 7.
    Lawrence, R.D., Almasi, G.S., Kotlyar, V., Viveros, M.S., Duri, S.: Personalization of supermarket product recommendations. Data Mining and Knowledge Discovery 5, 11–32 (2001)zbMATHCrossRefGoogle Scholar
  8. 8.
    Quadt, A.: Personalisierung im e-commerce. Diplomarbeit, AIFB, Universität Karlsruhe (TH), D-76128 Karlsruhe, Germany (2001)Google Scholar
  9. 9.
    Adamo, J.M.: Data Mining for Association Rules and Sequential Patterns. Springer, New York (2001)zbMATHCrossRefGoogle Scholar
  10. 10.
    Mild, A., Natter, M.: Collaborative filtering or regression models for internet recommendation systems? Journal of Targeting, Measurement and Analysis for Marketing 10, 304–313 (2002)CrossRefGoogle Scholar
  11. 11.
    Ehrenberg, A.S.C.: Repeat-Buying: Facts, Theory and Application. Charles Griffin & Company Ltd., London (1988)Google Scholar
  12. 12.
    Tan, P.N., Kumar, V.: Discovery of web robot sessions based on their navigational patterns. Data Mining and Knowledge Discovery 6, 9–35 (2002)CrossRefMathSciNetGoogle Scholar
  13. 13.
    Cooley, R., Mobasher, B., Srivastava, J.: Data preparation for mining world wide web browsing patterns. Journal of Knowledge and Information Systems 1, 5–32 (1999)Google Scholar
  14. 14.
    Cooley, R.W.: Web usage mining: Discovery and application of interesting patterns from web data. Ph.d. thesis, Graduate School of the University of Minnesota, University of Minnesota (2000)Google Scholar
  15. 15.
    Berendt, B., Mobasher, B., Spiliopoulou, M., Nakagawa, M.: The impact of site structure and user environment on session reconstruction in web usage analysis. In: Proceedings of the 4th WebKDD 2002 Workshop, at the ACM-SIGKDD Conference on Knowledge Discovery in Databases (KDD 2002), Edmonton, Alberta, Canada (2002)Google Scholar
  16. 16.
    Herlocker, J.L., Konstan, J.A., Borchers, A., Riedl, J.: Explaining collaborative filtering recommendations. In: Proceedings of the ACM 2000 Conference on Computer Supported Cooperative Work, pp. 241–250 (2000)Google Scholar
  17. 17.
    Kohavi, R., Provost, F.: Glossary of terms. Machine Learning 30, 271–274 (1988)Google Scholar
  18. 18.
    Salton, G., McGill, M.: Introduction to Modern Information Retrieval. McGraw-Hill, New York (1983)zbMATHGoogle Scholar
  19. 19.
    van Rijsbergen, C.: Information retrieval. Butterworth, London (1979)Google Scholar
  20. 20.
    Mobasher, B., Dai, H., Tao Luo, M.N.: Discovery and evaluation of aggregate usage profiles for web personalization. Data Mining and Knowledge Discovery 6, 61–82 (2002)CrossRefMathSciNetGoogle Scholar
  21. 21.
    Herlocker, J.L., Konstan, J.A., Borchers, A., Riedl, J.: An algorithmic framework for performing collaborative filtering. In: Proceedings of the 1999 Conference on Research and Development in Information Retrieval, pp. 230–237 (1999)Google Scholar
  22. 22.
    Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Application of dimensionality reduction in recommender systems–a case study. In: ACMWebKDD 2000Web Mining for E-Commerce Workshop (2000)Google Scholar
  23. 23.
    Mobasher, B., Dai, H., Luo, T., Nakagawa, M., Sun, Y., Wiltshire, J.: Discovery of aggregate usage profiles for web personalization. In: ACM WebKDD 2000 Web Mining for E-Commerce Workshop (2000)Google Scholar
  24. 24.
    Vucetic, S., Obradovic, Z.: A regression-based approach for scaling-up personalized recommender systems in e-commerce. In: ACM WebKDD 2000 Web Mining for E-Commerce Workshop (2000)Google Scholar
  25. 25.
    Yu, K., Xu, X., Ester, M., Kriegel, H.P.: Selecting relevant instances for efficient accurate collaborative filtering. In: Proceedings of the 10th CIKM, pp. 239–246. ACM Press, New York (2001)Google Scholar
  26. 26.
    Lin, W., Alvarez, S.A., Ruiz, C.: Efficient adaptive-support association rule mining for recommender systems. Data Mining and Knowledge Discovery 6, 83–105 (2002)CrossRefMathSciNetGoogle Scholar
  27. 27.
    Agrawal, R., Imielinski, T., Swami, A.: Mining associations between sets of items in large databases. In: Proc. of the ACM SIGMOD Int’l Conference on Management of Data, Washington D.C., pp. 207–216 (1993)Google Scholar
  28. 28.
    Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Bocca, J.B., Jarke, M., Zaniolo, C. (eds.) Proc. 20th Int. Conf. Very Large Data Bases, VLDB, Santiago, Chile, pp. 487–499 (1994)Google Scholar
  29. 29.
    Brin, S., Motwani, R., Ullman, J.D., Tsur, S.: Dynamic itemset counting and implication rules for market basket data. In: SIGMOD 1997, Proceedings ACM SIGMOD International Conference on Management of Data, Tucson, Arizona, USA, pp. 255–264 (1997)Google Scholar
  30. 30.
    Aggarwal, C.C., Yu, P.S.: A new framework for itemset generation. In: PODS 1998, Symposium on Principles of Database Systems, Seattle,WA, USA, pp. 18–24 (1998)Google Scholar
  31. 31.
    Bayardo, R., Agrawal, R., Gunopulos, D.: Constraint-based rule mining in large, dense databases. Data Mining and Knowledge Discovery 4, 217–240 (2000)CrossRefGoogle Scholar
  32. 32.
    Geyer-Schulz, A., Hahsler, M.: customer purchase incidence model applied to recommender systems. In: Kohavi, R., Masand, B., Spiliopoulou, M., Srivastava, J. (eds.) WebKDD 2001. LNCS (LNAI), vol. 2356, pp. 25–47. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  33. 33.
    Cheung, D.W., Jiawei, H., Ng, V.T., Wong, C.Y.: Maintenance of discovered association rules in large databases: An incremental updating technique. In: Proceedings of the 12th International Conference on Data Engineering, New Orleans., Piscataway, pp. 106–114. IEEE, Los Alamitos (1996)Google Scholar
  34. 34.
    Ng, K.K., Lam, W.: Updating of association rules dynamically. In: International Symposium on Database Applications in Non-Traditional Environments, 1999 (DANTE 1999) Proceedings, Piscataway, pp. 84–91. IEEE, Los Alamitos (2000)Google Scholar
  35. 35.
    Zheng, Z., Kohavi, R., Mason, L.: Real world performance of association rule algorithms. In: Provost, F., Srikant, R. (eds.) Proceedings of the 7th International Conference on Knowledge Discovery and Data Mining (ACM SIGKDD), pp. 401–406. ACM Press, New York (2001)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Andreas Geyer-Schulz
    • 1
  • Michael Hahsler
    • 2
  1. 1.Universität Karlsruhe (TH)KarlsruheGermany
  2. 2.Wirtschaftsuniversität WienWienAustria

Personalised recommendations