Predicting Product Purchase from Inferred Customer Similarity: An Autologistic Model Approach

Published Online:https://doi.org/10.1287/mnsc.1070.0760

References

  • Aerts M., Claeskens G. Boostrapping pseudolikelihood models for clustered binary data. Ann. Inst. Statist. Math. (1999) 51:515–560CrossrefGoogle Scholar
  • Andrews R. L., Ansari A., Currim I. S. Hierarchical Bayes versus finite mixture conjoint analysis models: A comparison of fit, prediction, and partworth recovery. J. Marketing Res. (2002) 39(1):87–98CrossrefGoogle Scholar
  • Ansari A., Mela C. F. E-Customization. J. Marketing Res. (2003) 40(2):131–145CrossrefGoogle Scholar
  • Ansari A., Essegaier S., Kohli R. Internet recommendation systems. J. Marketing Res. (2000) 37(3):363–375CrossrefGoogle Scholar
  • Ariely D., Lynch J. G., Aparicio M. Learning by collaborative and individual-based recommendation agents. J. Consumer Psych. (2004) 14(1–2):81–95CrossrefGoogle Scholar
  • Arnold B. C., Strauss D. Pseudolikelihood estimation: Some examples. Sankhya: Indian J. Statist. (1991) 53(Series B, Part 2):233–243Google Scholar
  • Arnold B. C., Castillo E., Sarabia J. M. Conditionally specified distributions: An introduction. Statist. Sci. (2001) 16(3):249–274CrossrefGoogle Scholar
  • Balabanovic M., Shoham Y. Fab: Content-based, collaborative recommendation. Comm. Assoc. Comput. Machinery (1997) 40(3):66–72CrossrefGoogle Scholar
  • Banerjee S., Carlin B. P., Gelfand A. E.Hierarchical Modeling and Analysis for Spatial Data (2004) (Chapman & Hall/CRC, Boca Raton, FL) . Monographs on Statistics and Applied Probability 101Google Scholar
  • Besag J. Spatial interaction and the statistical analysis of a lattice system. J. Roy. Statist. Soc., Ser. B (Methodological) (1974) 36:192–236Google Scholar
  • Bodapati A. Recommendation systems with purchase data. J. Marketing Res. (2008) 45(1CrossrefGoogle Scholar
  • Bronnenberg B. J., Mahajan V. Unobserved retailer behavior in multimarket data: Joint spatial dependence in market shares and promotion variables. Marketing Sci. (2001) 20(3):284–299LinkGoogle Scholar
  • Bronnenberg B. J., Sismeiro C. Using multimarket data to predict brand performance in markets for which no or poor data exist. J. Marketing Res. (2002) 39(1):1–17CrossrefGoogle Scholar
  • Cox D. R. The analysis of multivariate binary data. Appl. Statist. (J. Roy. Statist. Soc., Ser. C) (1972) 21(2):113–120Google Scholar
  • Cressie N. A. C.Statistics For Spatial Data (1993) (John Wiley & Sons, New York) . Wiley Series in Probability and Mathematical StatisticsCrossrefGoogle Scholar
  • Gershoff A. D., West P. M. Using a community of knowledge to build intelligent agents. Marketing Lett. (1998) 9(1):79–91CrossrefGoogle Scholar
  • Gruca T. S., Sudharshan D., Kumar K. R. Sibling brands, multiple objectives and responses to entry: The case of the Marion retail coffee market. J. Acad. Marketing Sci. (2002) 30(1):59–69CrossrefGoogle Scholar
  • Haining R.Spatial Data Analysis in the Social and Environmental Sciences (1990) (Cambridge University Press, Cambridge, UK) CrossrefGoogle Scholar
  • Hastie T., Tibshirani R., Friedman J.The Elements of Statistical Learning: Data Mining, Inference and Prediction (2001) (Springer-Verlag, New York) CrossrefGoogle Scholar
  • Herlocker J., Konstan J., Riedl J. An algorithmic framework for performing collaborative filtering. SIGIR'99: Proc. 22nd Annual Internat. ACM SIGIR Conf. Res. Development Inform. Retrieval (1999) (ACM Press, New York) 230–237CrossrefGoogle Scholar
  • Holbrook M. B., Moore W. L., Winer R. S. Constructing joint spaces from pick-any data: A new tool for customer analysis. J. Customer Res. (1982) 9(June):99–105Google Scholar
  • Iacobucci D., Arabie P., Bodapati A. Recommendation agents on the internet. J. Interactive Marketing (2000) 14(3):2–11CrossrefGoogle Scholar
  • Kamakura W. A., Ramaswami S. N., Srivastava R. K. Applying latent trait analysis in the evaluation of prospects for cross-selling of financial services. Internat. J. Res. Marketing (1991) 8:329–349CrossrefGoogle Scholar
  • Kamakura W. A., Wedel M., de Rosa F., Mazzon J. A. Cross-selling through database marketing. Internat. J. Res. Marketing (2003) 20:45–65CrossrefGoogle Scholar
  • Kapteyn A., Van De Geer S., Van De Stadt H., Wansbeek T. Interdependent preferences: An econometric analysis. J. Appl. Econometrics (1997) 12:665–686CrossrefGoogle Scholar
  • Kim Y. S., Street W. N., Russell G. J., Menczer F. Customer targeting: A neural network approach guided by genetic algorithms. Management Sci. (2005) 51(2):264–276LinkGoogle Scholar
  • Kwak K. Investigating the applicability of joint-space mapping to new product consideration and choice prediction. (2004) . Working paper, Tippie College of Management, University of Iowa, Iowa City, IAGoogle Scholar
  • Levine J. H. Joint-space analysis of “pick-any” data: Analysis of choices from an unconstrained set of alternatives. Psychometrika (1979) 44(1):85–92CrossrefGoogle Scholar
  • Mazis M. B., Ahtola O. T., Klippel R. E. A comparison of four multi-attribute models in the prediction of consumer attitudes. J. Consumer Res. (1975) 2(1):38–52CrossrefGoogle Scholar
  • Melville P., Mooney R. J., Nagarajan R. Content-boosted collaborative filtering for improved recommendations. Proc. Eighteenth National Conf. Artificial Intelligence (2002) (American Association for Artificial Intelligence, Menlo Park, CA) 187–192Google Scholar
  • Molenberghs G., Verbeke G.Models for Discrete Longitudinal Data (2005) (Springer, New York) Google Scholar
  • Moon S. Spatial choice models for product recommendations. (2003) . Doctoral thesis, Department of Marketing, Tippie School of Business, University of Iowa, Iowa City, IAGoogle Scholar
  • Mooney R. J., Roy L. Content-based book recommending using learning for text categorization. Proc. Fifth ACM Conf. Digital Libraries (2000) (ACM Press, New York) 195–204CrossrefGoogle Scholar
  • Moore W. L., Winer R. S. A panel-data based method for merging joint space and market response function estimation. Marketing Sci. (1987) 6(1):25–42LinkGoogle Scholar
  • Pazzani M. J. A framework for collaborative, content-based and demographic filtering. Artificial Intelligence Rev. (1999) 13:393–408CrossrefGoogle Scholar
  • Rossi P. E., McCulloch R. E., Allenby G. M. The value of purchase history data in target marketing. Marketing Sci. (1996) 15(4):321–340LinkGoogle Scholar
  • Russell G. J., Petersen A. Analysis of cross category dependence in market basket selection. J. Retailing (2000) 76(3):367–392CrossrefGoogle Scholar
  • Schafer J. B., Konstan J. A., Riedl J. E-commerce recommendation applications. Data Mining Knowledge Discovery (2001) 5:115–153CrossrefGoogle Scholar
  • Solomon M. R., Buchanan B. A role-theoretic approach to product symbolism: Mapping a consumption constellation. J. Bus. Res. (1991) 22:95–109CrossrefGoogle Scholar
  • Soucek B.Neural and Intelligent Systems Integration (1991) (John Wiley & Sons, New York) Google Scholar
  • Strauss D., Ikeda M. Pseudolikelihood estimation for social networks. J. Amer. Statist. Assoc. (1990) 85(March):204–212CrossrefGoogle Scholar
  • Yang S., Allenby G. M. Modeling interdependent consumer preferences. J. Marketing Res. (2003) 40(3):282–294CrossrefGoogle Scholar
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