Predicting Product Purchase from Inferred Customer Similarity: An Autologistic Model Approach
Published Online:1 Jan 2008https://doi.org/10.1287/mnsc.1070.0760
References
- Boostrapping pseudolikelihood models for clustered binary data. Ann. Inst. Statist. Math. (1999) 51:515–560Crossref, Google Scholar
- Hierarchical Bayes versus finite mixture conjoint analysis models: A comparison of fit, prediction, and partworth recovery. J. Marketing Res. (2002) 39(1):87–98Crossref, Google Scholar
- E-Customization. J. Marketing Res. (2003) 40(2):131–145Crossref, Google Scholar
- Internet recommendation systems. J. Marketing Res. (2000) 37(3):363–375Crossref, Google Scholar
- Learning by collaborative and individual-based recommendation agents. J. Consumer Psych. (2004) 14(1–2):81–95Crossref, Google Scholar
- Pseudolikelihood estimation: Some examples. Sankhya: Indian J. Statist. (1991) 53(Series B, Part 2):233–243Google Scholar
- Conditionally specified distributions: An introduction. Statist. Sci. (2001) 16(3):249–274Crossref, Google Scholar
- Fab: Content-based, collaborative recommendation. Comm. Assoc. Comput. Machinery (1997) 40(3):66–72Crossref, Google Scholar
- Hierarchical Modeling and Analysis for Spatial Data (2004) (Chapman & Hall/CRC, Boca Raton, FL) . Monographs on Statistics and Applied Probability 101Google Scholar
- Spatial interaction and the statistical analysis of a lattice system. J. Roy. Statist. Soc., Ser. B (Methodological) (1974) 36:192–236Google Scholar
- Recommendation systems with purchase data. J. Marketing Res. (2008) 45(1Crossref, Google Scholar
- Unobserved retailer behavior in multimarket data: Joint spatial dependence in market shares and promotion variables. Marketing Sci. (2001) 20(3):284–299Link, Google Scholar
- Using multimarket data to predict brand performance in markets for which no or poor data exist. J. Marketing Res. (2002) 39(1):1–17Crossref, Google Scholar
- The analysis of multivariate binary data. Appl. Statist. (J. Roy. Statist. Soc., Ser. C) (1972) 21(2):113–120Google Scholar
- Statistics For Spatial Data (1993) (John Wiley & Sons, New York) . Wiley Series in Probability and Mathematical StatisticsCrossref, Google Scholar
- Using a community of knowledge to build intelligent agents. Marketing Lett. (1998) 9(1):79–91Crossref, Google Scholar
- Sibling brands, multiple objectives and responses to entry: The case of the Marion retail coffee market. J. Acad. Marketing Sci. (2002) 30(1):59–69Crossref, Google Scholar
- Spatial Data Analysis in the Social and Environmental Sciences (1990) (Cambridge University Press, Cambridge, UK) Crossref, Google Scholar
- The Elements of Statistical Learning: Data Mining, Inference and Prediction (2001) (Springer-Verlag, New York) Crossref, Google Scholar
- 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) 230–237Crossref, Google Scholar
- Constructing joint spaces from pick-any data: A new tool for customer analysis. J. Customer Res. (1982) 9(June):99–105Google Scholar
- Recommendation agents on the internet. J. Interactive Marketing (2000) 14(3):2–11Crossref, Google Scholar
- Applying latent trait analysis in the evaluation of prospects for cross-selling of financial services. Internat. J. Res. Marketing (1991) 8:329–349Crossref, Google Scholar
- Cross-selling through database marketing. Internat. J. Res. Marketing (2003) 20:45–65Crossref, Google Scholar
- Interdependent preferences: An econometric analysis. J. Appl. Econometrics (1997) 12:665–686Crossref, Google Scholar
- Customer targeting: A neural network approach guided by genetic algorithms. Management Sci. (2005) 51(2):264–276Link, Google Scholar
- 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
- Joint-space analysis of “pick-any” data: Analysis of choices from an unconstrained set of alternatives. Psychometrika (1979) 44(1):85–92Crossref, Google Scholar
- A comparison of four multi-attribute models in the prediction of consumer attitudes. J. Consumer Res. (1975) 2(1):38–52Crossref, Google Scholar
- 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
- Models for Discrete Longitudinal Data (2005) (Springer, New York) Google Scholar
- Spatial choice models for product recommendations. (2003) . Doctoral thesis, Department of Marketing, Tippie School of Business, University of Iowa, Iowa City, IAGoogle Scholar
- Content-based book recommending using learning for text categorization. Proc. Fifth ACM Conf. Digital Libraries (2000) (ACM Press, New York) 195–204Crossref, Google Scholar
- A panel-data based method for merging joint space and market response function estimation. Marketing Sci. (1987) 6(1):25–42Link, Google Scholar
- A framework for collaborative, content-based and demographic filtering. Artificial Intelligence Rev. (1999) 13:393–408Crossref, Google Scholar
- The value of purchase history data in target marketing. Marketing Sci. (1996) 15(4):321–340Link, Google Scholar
- Analysis of cross category dependence in market basket selection. J. Retailing (2000) 76(3):367–392Crossref, Google Scholar
- E-commerce recommendation applications. Data Mining Knowledge Discovery (2001) 5:115–153Crossref, Google Scholar
- A role-theoretic approach to product symbolism: Mapping a consumption constellation. J. Bus. Res. (1991) 22:95–109Crossref, Google Scholar
- Neural and Intelligent Systems Integration (1991) (John Wiley & Sons, New York) Google Scholar
- Pseudolikelihood estimation for social networks. J. Amer. Statist. Assoc. (1990) 85(March):204–212Crossref, Google Scholar
- Modeling interdependent consumer preferences. J. Marketing Res. (2003) 40(3):282–294Crossref, Google Scholar

