A customer purchase incidence model applied to recommender services

Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

Volumn 2356, Issue , 2002, Pages 25-47

  Geyer Schulz, Andreas ^a   Hahsler, Michael ^b   Jahn, Maximillian ^b  

^a UNIVERSITY OF KARLSRUHE   (Germany)

^b VIENNA UNIVERSITY OF ECONOMICS AND BUSINESS   (Austria)

Author keywords

[No Author keywords available]

Indexed keywords

COMMERCE; CONSUMER PRODUCTS;

BUSINESS ADMINISTRATION; ELECTRONIC MARKET; EMPIRICAL VALIDATION; INFORMATION GOODS; INFORMATION MARKET; MODEL-BASED OPC; VIRTUAL UNIVERSITY; WEB-BASED INFORMATION;

SALES;

EID: 0742309128     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/3-540-45640-6_2     Document Type: Conference Paper

References (33)

1. Aggrawall, C.C., Yu, P.S.: A New Approach to Online Generation of Association Rules. IEEE Trans. on Knowledge Eng. 13(4) (2001) 527-540.
2. Agrawal, R., Imielinski, T., Swami, A.: Mining Association Rules between Sets of Items in Large Databases. Proc. of the 1993 ACM SIGMOD Int'l Conf. on Management of Data (1994) 207-216.
3. Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules. Proc. 20th Int'l Conf. on Very Large Databases (1994) 478-499.
4. Aske Research: The Structure of the Tooth-Paste Market. Aske Research Ltd., London (1975).
5. Avery, C., Zeckhauser, R.: Recommender Systems for Evaluating Computer Messages. CACM 40(3) (1997) 88-89.
6. Bellmann, S., Lohse, G.L., Johnson, E.J.: Predictors of Online Buying Behavior. CACM 42(12) (1999) 32-38.
7. Blischok, T.J.: Every Transaction Tells a Story. Chain Store Age Executive 71(3) (1995) 50-62.
8. Böhm, W., Geyer-Schulz, A., Hahsler, M., Jahn, M.: Repeat Buying Theory and its Application for Recommender Services. In: Opitz, O. (Ed.): Studies in Classification, Data Analysis, and Knowledge Organization, Springer, Heidelberg (to appear).
9. Chatfield, C., Ehrenberg, A. S. C., Goodhardt, G. J.: Progress on a Simplified Model of Stationary Purchasing Behavior. J. of the Royal Stat. Society A 129 (1966) 317-367.
10. Charlton, P., Ehrenberg, A. S. C.: Customers of the LEP, Appl. Statist. 25 (1976) 26-30.
11. Ehrenberg, A. S. C.: Repeat-Buying: Facts, Theory and Applications. Charles Griffin & Company Limited, London (1988).
12. Feller, W.: An Introduction to Probability Theory and Its Applications. Vol. 2. John Wiley & Sons, New York (1971).
13. Ganter, B., Wille, R.: Formal Concept Analysis: Mathematical Foundations. Springer, Heidelberg (1999).
14. Gaul, W., Schmidt-Thieme, L.: Recommender Systems Based on User Navigational Behavior in the Internet. Behaviormetrika 29(1) 2002 to appear.
15. Geyer-Schulz, A., Hahsler, M., Jahn, M.: my VU: A Next Generation Recommender System Based on Observed Consumer Behavior and Interactive Evolutionary Algorithms. In: Gaul, W., Opitz, O., Schader, M. (Eds.): Data Analysis - Scientific Modeling and Practical Applications, Studies in Classification, Data Analysis, and Knowledge Organization, Vol. 18, Springer, Heidelberg (2000) 447-457.
16. Geyer-Schulz, A., Hahsler, M., Jahn, M.: Educational and Scientific Recommender Systems: Designing the Information Channels of the Virtual University. Int. J. of Engineering Education 17(2) (2001) 153-163.
17. Geyer-Schulz, A., Hahsler, M., Jahn, M.: Recommendations for Virtual Universities from Observed User Behavior. In: Gaul, W., Ritter, G., Schader, M. (Eds.): Studies in Classification, Data Analysis, and Knowledge Organization, Springer, Heidelberg (to appear).
18. Goodhardt, G.J., Ehrenberg, A.S.C., Collins, M.A.: The Dirichlet: A Comprehensive Model of Buying Behaviour. J. of the Royal Stat. Society A 147 (1984) 621-655.
19. Johnson, N.L., Kotz, S.: Discrete Distributions. Houghton Mifflin, Boston (1969).
20. Johnson, N.L., Kotz, S., Balakrishnan, N.: Discrete Multivariate Distributions. John Wiley & Sons, New York (1997).
21. Lawrence, R.D., Almasi, G.S., Kotlyar, V., Viveros, M.S., Duri, S.S.: Personalization of Supermarket Product Recommendations. Data Mining and Knowledge Discovery 5 (2001) 11-32.
22. Lin, W., Alvarez, S.A., Ruiz, C.: Efficient Adaptive-Support Association Rule Mining for Recommender Systems. Data Mining and Knowledge Discovery 6(1) (2002) 83-105.
23. Mobasher, B., Cooley, R., Srivastava, J.: Automatic Personalization Based on Web Usage Mining. CACM 43(8) (2000) 142-151.
24. Mobasher, B., Dai, H., Luo, T., Nakagawa, M.: Discovery and Evaluation of Aggregate Usage Profiles for Web Personalization. Data Mining and Knowledge Discovery 6 (2002) 61-82.
25. Resnick, P., Varian, H.R. (1997): Recommender Systems. CACM 40(3) (1997) 56-58.
26. Powell, N., Westwood, J.: Buyer-Behaviour in Management Education. Appl. Statist. 27 (1978) 69-72.
27. Sichel, H. S.: Repeat-Buying and the Poisson-Generalised Inverse Gaussion Distributions. Appl. Statist. 31 (1982) 193-204.
28. Spiliopoulou, M.:Web Usage Mining forWeb Site Evaluation. CACM 43(8) (2000) 127-134.
29. Srivastava, J., Cooley, R., Deshpande, M., Tan P.-N.:Web Usage Mining: Discovery and Applications of Usage Patterns from Web Data. SIGKDD Explorations 1(2) (2000) 1-12.
30. S.R.S.: The S.R.S. Motorists Panel. S.R.S. Sales Research Service, London (1965).
31. Stumme, G., Taouil, R., Bastide Y., Pasquier N., Lakhal L.: Computing Iceberg Concept Lattices with TITANIC. J. on Knowledge and Data Engineering to appear.
32. Wagner, U., Taudes, A.: Stochastic Models of Consumer Behaviour. Europ. J. of Op. Res. 29(1) (1987) 1-23.
33. Yen, S.J., Chen, A.L.P.: A Graph-Based Approach for Discovering Various Types of Association Rules. IEEE Trans. on Knowledge Eng. 13(5) (2001) 839-845.