Internet Recommendation Systems

Several online firms, including Yahoo!, Amazon.com, and Movie Critic, recommend documents and products to consumers. Typically, the recommendations are based on content and/or collaborative filtering methods. The authors examine the merits of these methods, suggest that preference models used in marketing offer good alternatives, and describe a Bayesian preference model that allows statistical integration of five types of information useful for making recommendations: a person's expressed preferences, preferences of other consumers, expert evaluations, item characteristics, and individual characteristics. The proposed method accounts for not only preference heterogeneity across users but also unobserved product heterogeneity by introducing the interaction of unobserved product attributes with customer characteristics. The authors describe estimation by means of Markov chain Monte Carlo methods and use the model with a large data set to recommend movies either when collaborative filtering methods are viable alternatives or when no recommendations can be made by these methods.

[1]  Nicholas Negroponte,et al.  The architecture machine , 1975, Comput. Aided Des..

[2]  R. Rosner.  Computer software , 1978, Nature.

[3]  Gerard Salton,et al.  Term-Weighting Approaches in Automatic Text Retrieval , 1988, Inf. Process. Manag..

[4]  Adrian F. M. Smith,et al.  Sampling-Based Approaches to Calculating Marginal Densities , 1990 .

[5]  Douglas B. Terry,et al.  Using collaborative filtering to weave an information tapestry , 1992, CACM.

[6]  S. Chib,et al.  Bayesian analysis of binary and polychotomous response data , 1993 .

[7]  Pattie Maes,et al.  Social information filtering: algorithms for automating “word of mouth” , 1995, CHI '95.

[8]  Greg M. Allenby,et al.  Using Extremes to Design Products and Segment Markets , 1995 .

[9]  J. Eliashberg,et al.  A Parsimonious Model for Forecasting Gross Box-Office Revenues of Motion Pictures , 1996 .

[10]  Peter E. Rossi,et al.  The Value of Purchase History Data in Target Marketing , 1996 .

[11]  Alan E. Gelfand,et al.  Model Determination using sampling-based methods , 1996 .

[12]  Steven M. Shugan,et al.  Film Critics: Influencers or Predictors? , 1997 .

[13]  Sylvia Richardson,et al.  Markov Chain Monte Carlo in Practice , 1997 .

[14]  Yoav Shoham,et al.  Fab: content-based, collaborative recommendation , 1997, CACM.

[15]  Andrew D. Gershoff,et al.  Using a Community of Knowledge to Build Intelligent Agents , 1998 .

[16]  David Heckerman,et al.  Empirical Analysis of Predictive Algorithms for Collaborative Filtering , 1998, UAI.

[17]  Bradley N. Miller,et al.  Using filtering agents to improve prediction quality in the GroupLens research collaborative filtering system , 1998, CSCW '98.

[18]  N. G. Best,et al.  Bayesian deviance the e ective number of parameters and the comparison of arbitrarily complex models , 1998 .

[19]  Robert E. Krider,et al.  Clustering at the Movies , 1998 .

[20]  Eric T. Bradlow,et al.  Agents to the Rescue? , 1999 .

[21]  Edward I. George,et al.  A bayesian model for collaborative filtering , 1999, AISTATS.