Design and implementation of user context aware recommendation engine for mobile using Bayesian network, fuzzy logic and rule base

Thyagaraju G.S., U.P. Kulkarni

International Journal of Pervasive Computing and Communications

ISSN: 1742-7371

Publication date: 22 June 2012 

The authors formulate the service adaptation process by using artificial intelligence techniques like Bayesian Network, fuzzy logic and rule based reasoning. Bayesian Network is used to classify the incoming call (high priority call, low priority call and unknown calls), fuzzy linguistic variables and membership degrees to define the context situations, the rules for adopting the policies of implementing a service, fitness degree computation and service recommendation. In addition to this the paper proposes maximum to minimum priority based context attributes matching algorithm for rule selection based on fitness degree of rules. The context aware mobile is tested for library and class room scenario to exemplify the proposed service recommendation engine and demonstrate its effectiveness.

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