Analyzing Consumer-Product Graphs: Empirical Findings and Applications in Recommender Systems
Published Online:1 Jul 2007https://doi.org/10.1287/mnsc.1060.0619
References
- Using data mining methods to build customer profiles. IEEE Comput. (2001) 34(2):74–82Crossref, Google Scholar
- Statistical mechanics of complex networks. Rev. Modern Phys. (2002) 74:47–97Crossref, Google Scholar
- Error and attack tolerance of complex networks. Nature (2000) 406:378–382Crossref, Google Scholar
- Using extremes to design products and segment markets. J. Marketing Res. (1995) 32(4):392–403Crossref, Google Scholar
- Classes of small-world networks. Proc. Natl. Acad. Sci. USA (2000) 97(21):11149–11152Crossref, Google Scholar
- Internet recommendations systems. J. Marketing Res. (2000) 37(3):363–375Crossref, Google Scholar
- Spectral analysis of data. Proc. 33rd ACM Sympos. Theory Comput. (2001) (ACM Press, New York) 619–626Crossref, Google Scholar
- Emergence of scaling in random networks. Science (1999) 286:509–512Crossref, Google Scholar
- Evolution of the social network of scientific collaborations. Physica A (2002) 311:590–614Crossref, Google Scholar
- Where do small worlds come from? Indust. Corporate Change (2003) 12:697–725Crossref, Google Scholar
- Predictors of online buying behavior. Comm. ACM (1999) 42(12):32–38Crossref, Google Scholar
- Graph Theory with Applications (1976) (American Elsevier Publishing, New York) Crossref, Google Scholar
- Bayesian lifetime model for the “Hot 100” billboard songs. J. Amer. Statist. Assoc. (2001) 96:368–381Crossref, Google Scholar
- Empirical analysis of predictive algorithms for collaborative filtering. Proc. Fourteenth Conf. Uncertainty Artificial Intelligence (1998) (Morgan Kaufmann, San Francisco, CA) 43–52Google Scholar
- The small world of human language. Proc. Roy. Soc. London Ser. B—Biol. Sci. (2001) 268:2261–2265Crossref, Google Scholar
- The small world network structure of boards of directors. (2004) . Social Science Research Network, http://ssrn.com/abstract=546963Google Scholar
- Regression models and life-tables (with discussion). J. Roy. Statist. Soc. (1972) B34:187–220Google Scholar
- The small world of the corporate elite, 1982–2001. Strategic Organ. (2003) 1:301–326Crossref, Google Scholar
- Maximum likelihood from incomplete data via the EM algorithm. J. Roy. Statist. Soc. Ser. B (1977) 39(1):1–38Google Scholar
- Item-based top-N recommendation algorithms. ACM Trans. Inform. Systems (2004) 22(1):143–177Crossref, Google Scholar
- Scaling properties of scale-free evolving networks: Continuous approach. Phys. Rev. E (2001) 63(5):056125-1–056125-19Crossref, Google Scholar
- Repeat-Buying: Facts, Theory and Applications (1988) (Charles Griffin & Company Limited, London, UK) Google Scholar
- On random graphs. Pub. Math. (1959) 6:290–297Google Scholar
- Forecasting repeat sales at CDNOW: A case study. Interfaces (2001) 31(3):S94–S107Link, Google Scholar
- On power law relationships of the Internet topology. Proc. ACM SIGCOMM. (1999) (ACM Press, New York) 251–262Crossref, Google Scholar
- Using a community of knowledge to build intelligent agents. Marketing Lett. (1998) 9(1):79–91Crossref, Google Scholar
- The science of the sleeper: How the information age could blow away the blockbuster. The New Yorker (1999) October 4):48–55Google Scholar
- Eigentaste: A constant time collaborative filtering algorithm. Inform. Retrieval (2001) 4(2):133–151Crossref, Google Scholar
- Team assembly mechanisms determine collaboration network structure and team performance. Science (2005) 308(5722):697–702Crossref, Google Scholar
- Dependency networks for inference, collaborative filtering, and data visualization. J. Machine Learn. Res. (2000) 1:49–75Google Scholar
- Evaluating collaborative filtering recommender systems. ACM Trans. Inform. Systems (2004) 22(1):5–53Crossref, Google Scholar
- Recommending and evaluating choices in a virtual community of use. Proc. ACM Conf. Human Factors in Comput. Systems CHI’95 (1995) (ACM Press, New York) 194–201Crossref, Google Scholar
- Latent semantic models for collaborative filtering. ACM Trans. Inform. Systems (2004) 22(1):89–115Crossref, Google Scholar
- Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering. ACM Trans. Inform. Systems (TOIS) (2004) 22(1):116–142Crossref, Google Scholar
- Investigating household purchase timing decisions: A conditional hazard function approach. Marketing Sci. (1991) 10:1–23Link, Google Scholar
- A random-coefficients logit brand-choice model applied to panel data. J. Bus. Econom. Statist. (1994) 12:317–328Crossref, Google Scholar
- Lethality and centrality in protein networks. Nature (2001) 411:41–42Crossref, Google Scholar
- A probabilistic choice model for market segmentation and elasticity structure. J. Marketing Res. (1989) 26:379–390Crossref, Google Scholar
- Concomitant variable latent class models for conjoint analysis. Internat. J. Res. Marketing (1994) 11:451–464Crossref, Google Scholar
- Multilevel k-way partitioning scheme for irregular graphs. J. Parallel Distributed Comput. (1998) 48:96–129Crossref, Google Scholar
- The small world of Germany and the durability of national ownership networks. Amer. Sociol. Rev. (2001) 66(3):317–335Crossref, Google Scholar
- Recommendation systems: A probabilistic analysis. Proc. 39th Annual Sympos. Foundations Comput. Sci. (1998) (IEEE Computer Society Press, Los Alamitos, CA) 664–673Crossref, Google Scholar
- Efficient adaptive-support association rule mining for recommender systems. Data Mining Knowledge Discovery (2002) 6:83–105Crossref, Google Scholar
- , Zarembka P. Conditional logit analysis of qualitative choice behavior. Frontiers of Econometrics (1974) (Academic Press, New York) 105–142Google Scholar
- The structure and function of complex networks. SIAM Rev. (2003) 45(2):167–256Crossref, Google Scholar
- Random graphs with arbitrary degree distributions and their applications. Phys. Rev. E (2001) 64(2):026118-1–026118-17Crossref, Google Scholar
- Random graph models of social networks. Proc. Natl. Acad. Sci. USA (2002) 99:2566–2572Crossref, Google Scholar
- Recommender systems. Comm. ACM (1997) 40(3):56–58Crossref, Google Scholar
- GroupLens: An open architecture for collaborative filtering of netnews. Proc. ACM Conf. Comput.-Supported Cooperative Work (1994) (ACM Press, New York) 175–186Crossref, Google Scholar
- Small worlds among interlocking directors: Network structure and distance in bipartite graphs. Comput. Math. Organ. Theory (2004) 10:69–94Crossref, Google Scholar
- The value of purchase history data in target marketing. Marketing Sci. (1996) 15(4):321–340Link, Google Scholar
- Application of dimensionality reduction in recommender systems: A case study. Proc. WebKDD Workshop ACM SIGKKD (2000) (ACM Press, New York) Crossref, Google Scholar
- E-commerce recommendation applications. Data Mining Knowledge Discovery (2001) 5(1–2):115–153Crossref, Google Scholar
- Social information filtering: Algorithms for automating word of mouth. Proc. ACM Conf. Human Factors Comput. Systems (1995) (ACM Press, New York) 210–217Crossref, Google Scholar
- A formal statistical approach to collaborative filtering. Proc. Conf. Automated Learn. Discovery (CONALD’98) (1998) Pittsburgh, PA http://citeseer.ist.psu.edu/ungar98formal.htmlGoogle Scholar
- Collaboration and creativity: The small world problem. Amer. J. Sociol. (2005) 111(2):447–504Crossref, Google Scholar
- Word of Mouse: The Marketing Power of Collaborative Filtering (2002) (Warner Business Books, New York) Google Scholar
- Small Worlds: The Dynamics of Networks Between Order and Randomness (1999) (Princeton University Press, Princeton, NJ) Crossref, Google Scholar
- Collective dynamics of small-world networks. Nature (1998) 393:440–442Crossref, Google Scholar
- Emerging behavior in electronic bidding. Phys. Rev. Lett. E (2003) 68(1):016102-1–016102-5Google Scholar

