Relational bayesian modeling for electronic commerce
AssigneeArt Technology Group, Inc.
InventorBruce Douglass D'Ambrosio
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Abstract
The present invention provides a language, method and system to formulate and evaluate relational Bayesian networks in an c-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.
Claims35
A method for automatically generating a relational Bayesian network to model events using synthetic variables, comprising the acts of:
- providing a language to represent and create a plurality of synthetic variables;
- providing an inference using the plurality of synthetic variables;
- and
- providing a discovery of the plurality of synthetic variables.
A method for searching a database to automatically generating a relational Bayesian network model using synthetic variables comprising:
- providing a database of information;
- providing a relational Bayesian network model with the information;
- providing a language to create a plurality of synthetic variables from the relational Bayesian network model;
- providing an inference using the plurality of synthetic variables;
- and
- providing a discovery of the plurality of synthetic variables.
A system for automatically generating a relational Bayesian network model using synthetic variables comprising:
- a database of information;
- a language to create a plurality of synthetic variables from the database of information;
- and
- a processor that provides an inference using the plurality of synthetic variables, wherein the processor further provides a discovery of the plurality of synthetic variables and creates a relational Bayesian network model.
A method for creating a relational Bayesian network to model a user's real-time behavior in an e-commerce environment comprising the acts of:
- receiving real-time information regarding a user's web-behavior;
- constructing a relational Bayesian network to model and predict a user's real-time web-behavior;
- and
- modeling a user's real-time web-behavior by evaluating the Bayesian network.
Dependencies are inferred from the claim text.