A Decision-Based Approach for Recommending in Hierarchical Domains

Recommendation Systems are tools designed to help users to find items within a given domain, according to their own preferences expressed by means of a user profile. A general model for recommendation systems based on probabilistic graphical models is proposed in this paper. It is designed to deal with hierarchical domains, where the items can be grouped in a hierarchy, each item being only contained in another, more general item. The model makes decisions about which items in the hierarchy are more useful for the user, and carries out the necessary computations in a very efficient way.

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

[2]  Prakash P. Shenoy,et al.  A new method for representing and solving Bayesian decision problems , 1993 .

[3]  D. J. Hand,et al.  Artificial Intelligence Frontiers in Statistics: AI and Statistics III , 1992 .

[4]  Finn V. Jensen,et al.  Bayesian Networks and Decision Graphs , 2001, Statistics for Engineering and Information Science.

[5]  C. Butz,et al.  A Bayesian Approach to User Profiling in Information Retrieval , 2000 .

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

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

[8]  Luis M. de Campos,et al.  Clustering terms in the Bayesian network retrieval model: a new approach with two term-layers , 2004, Appl. Soft Comput..

[9]  Luis M. de Campos,et al.  A Multi-layered Bayesian Network Model for Structured Document Retrieval , 2003, ECSQARU.

[10]  María S. Pérez-Hernández,et al.  Collaborative Filtering Using Interval Estimation Naïve Bayes , 2003, AWIC.

[11]  Michael Clarke,et al.  Symbolic and Quantitative Approaches to Reasoning and Uncertainty , 1991, Lecture Notes in Computer Science.

[12]  Luis M. de Campos,et al.  The BNR model: foundations and performance of a Bayesian network-based retrieval model , 2003, Int. J. Approx. Reason..

[13]  Petri Nokelainen,et al.  Optimizing and profiling users online with Bayesian probabilistic modeling , 2002 .

[14]  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).

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

[16]  Y. Shoham,et al.  Ecom Syst Content-based, Collaborative Recommendation , 1997 .