Managing uncertainty in group recommending processes

AbstractWhile the problem of building recommender systems has attracted considerable attention in recent years, most recommender systems are designed for recommending items to individuals. The aim of this paper is to automatically recommend a ranked list of new items to a group of users. We will investigate the value of using Bayesian networks to represent the different uncertainties involved in a group recommending process, i.e. those uncertainties related to mechanisms that govern the interactions between group members and the processes leading to the final choice or recommendation. We will also show how the most common aggregation strategies might be encoded using a Bayesian network formalism. The proposed model can be considered as a collaborative Bayesian network-based group recommender system, where group ratings are computed from the past voting patterns of other users with similar tastes.

[1]  Gediminas Adomavicius,et al.  Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions , 2005, IEEE Transactions on Knowledge and Data Engineering.

[2]  David Maxwell Chickering,et al.  Dependency Networks for Inference, Collaborative Filtering, and Data Visualization , 2000, J. Mach. Learn. Res..

[3]  Luis M. de Campos,et al.  A collaborative recommender system based on probabilistic inference from fuzzy observations , 2008, Fuzzy Sets Syst..

[4]  Joseph F. McCarthy,et al.  MUSICFX: an arbiter of group preferences for computer supported collaborative workouts , 2000, CSCW '00.

[5]  AdomaviciusGediminas,et al.  Toward the Next Generation of Recommender Systems , 2005 .

[6]  Analía Amandi,et al.  User profiling with Case-Based Reasoning and Bayesian Networks , 2000, IBERAMIA-SBIA 2000 Open Discussion Track.

[7]  Michael P. Wellman,et al.  Graphical Models for Groups: Belief Aggregation and Risk Sharing , 2005, Decis. Anal..

[8]  Anthony Jameson,et al.  More than the sum of its members: challenges for group recommender systems , 2004, AVI.

[9]  Ingrid Zukerman,et al.  Introduction to the special issue on statistical and probabilistic methods for user modeling , 2007, User Modeling and User-Adapted Interaction.

[10]  Liliana Ardissono,et al.  Intrigue: Personalized recommendation of tourist attractions for desktop and hand held devices , 2003, Appl. Artif. Intell..

[11]  John Riedl,et al.  An algorithmic framework for performing collaborative filtering , 1999, SIGIR '99.

[12]  John Riedl,et al.  PolyLens: A recommender system for groups of user , 2001, ECSCW.

[13]  George M. Giaglis,et al.  A hybrid approach for improving predictive accuracy of collaborative filtering algorithms , 2007, User Modeling and User-Adapted Interaction.

[14]  Paul Resnick,et al.  Recommender systems , 1997, CACM.

[15]  Alfred Kobsa,et al.  The Adaptive Web, Methods and Strategies of Web Personalization , 2007, The Adaptive Web.

[16]  Barry Smyth,et al.  Recommendation to Groups , 2007, The Adaptive Web.

[17]  David M. Pennock,et al.  Probabilistic Models for Unified Collaborative and Content-Based Recommendation in Sparse-Data Environments , 2001, UAI.

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

[19]  Michael J. Pazzani,et al.  Collaborative Filtering with the Simple Bayesian Classifier , 2000, PRICAI.

[20]  Xingshe Zhou,et al.  TV Program Recommendation for Multiple Viewers Based on user Profile Merging , 2006, User Modeling and User-Adapted Interaction.

[21]  Jonathan L. Herlocker,et al.  Evaluating collaborative filtering recommender systems , 2004, TOIS.

[22]  Robert L. Winkler,et al.  Combining Probability Distributions From Experts in Risk Analysis , 1999 .

[23]  Judith Masthoff,et al.  In pursuit of satisfaction and the prevention of embarrassment: affective state in group recommender systems , 2006, User Modeling and User-Adapted Interaction.

[24]  Daniel Kudenko,et al.  Group Decision Making through Mediated Discussions , 2003, User Modeling.

[25]  Loriene Roy,et al.  Content-based book recommending using learning for text categorization , 1999, DL '00.

[26]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[27]  Yen-Liang Chen,et al.  A group recommendation system with consideration of interactions among group members , 2008, Expert Syst. Appl..

[28]  Christian Genest,et al.  Combining Probability Distributions: A Critique and an Annotated Bibliography , 1986 .

[29]  Ingrid Zukerman,et al.  # 2001 Kluwer Academic Publishers. Printed in the Netherlands. Predictive Statistical Models for User Modeling , 1999 .

[30]  Cory J. Butz Exploiting contextual independencies in Web search and user profiling , 2002, 2002 IEEE World Congress on Computational Intelligence. 2002 IEEE International Conference on Fuzzy Systems. FUZZ-IEEE'02. Proceedings (Cat. No.02CH37291).

[31]  Andrés R. Masegosa,et al.  Combining Decision Trees Based on Imprecise Probabilities and Uncertainty Measures , 2007, ECSQARU.

[32]  Judith Masthoff,et al.  Group Modeling: Selecting a Sequence of Television Items to Suit a Group of Viewers , 2004, User Modeling and User-Adapted Interaction.

[33]  Jiri Matas,et al.  On Combining Classifiers , 1998, IEEE Trans. Pattern Anal. Mach. Intell..