Relational Bayesian modeling for electronic commerce

Details Relational Bayesian modeling for electronic commerce Relational Bayesian modeling for electronic commerce

2010-11-17

2011-10-25

Starks, Wilbert L (Department: 2129)

Data processing: artificial intelligence

Neural network

Learning task

C706S045000

Reexamination Certificate

active

08046315

ABSTRACT:
The present invention provides a language, method and system to formulate and evaluate relational Bayesian networks in an e-commerce environment. The present invention employs a specific language for constructing synthetic variables used to predict events in the Bayesian networks. The present system and language allow for efficient and accurate representation, inference, and discovery of the synthetic variables used to model web visitor behavior.

REFERENCES:
D'Ambrosio, B. et al., Probabilistic Relational Models of On-line User Behavior: Early Explorations, Proceedings of the Fifth WEBKDD workshop: Webmining as a Premise to Effective and Intelligent Web Applications (WEBKDD'2003), in conjunction with ACM SIGKDD conference, Washington, DC, USA, Aug. 27, 2003, pp. 9-16.
Greenspan, H., et al., Learning Texture Discrimination Rules in a Multiresolution System, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 16, No. 9, pp. 894-901, Sep. 1994.

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