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This monograph proposes a model of Tourist
recommender comprising of conventional Neural Network
and Rough set construct; in turn this makes the
system intelligent and adaptive. The case study
considered in this report is the tourism practices
followed in the North West state of India, Rajasthan.
Present case study also has the flavor of
multi-criteria driven recommender system and modeled
through a heuristics method combining both Rough set
and Neural Network. The potential of rough set to
handle uncertain request of the tourists choices has
been demonstrated compared to the conventional usage
of Rough Set as Clustering.
The work also encourages the complete shell
development of such recommender system which could be
thus used in other diversified and complex
recommendation of medical service after modifying its
contextual knowledge. It has been observed from the
proposed model that tourist's choices could be
combined optimally with the help of plug-in software
included in web-portal to serve them better.
The elaborated background of recommender system, its
components and state-of-the art, mathematical
illustrations has been presented in the Appendix of
the report.
recommender comprising of conventional Neural Network
and Rough set construct; in turn this makes the
system intelligent and adaptive. The case study
considered in this report is the tourism practices
followed in the North West state of India, Rajasthan.
Present case study also has the flavor of
multi-criteria driven recommender system and modeled
through a heuristics method combining both Rough set
and Neural Network. The potential of rough set to
handle uncertain request of the tourists choices has
been demonstrated compared to the conventional usage
of Rough Set as Clustering.
The work also encourages the complete shell
development of such recommender system which could be
thus used in other diversified and complex
recommendation of medical service after modifying its
contextual knowledge. It has been observed from the
proposed model that tourist's choices could be
combined optimally with the help of plug-in software
included in web-portal to serve them better.
The elaborated background of recommender system, its
components and state-of-the art, mathematical
illustrations has been presented in the Appendix of
the report.