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International Journal of Technology Enhancements and Emerging Engineering Research (ISSN 2347-4289)
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International Journal of Technology Enhancements and Emerging Engineering Research  
International Journal of Technology Enhancements and Emerging Engineering Research

Website: http://www.ijteee.org

ISSN 2347-4289



Knowledge Discovery From Expert Profile Processing - The Expert Finding Solutions For Pet Domain

[Full Text]

 

AUTHOR(S)

Minakshi Gujral, Satish Chandra

 

KEYWORDS

Keywords : Expert finding systems; Knowledge discovery; Recommendation systems; Information Systems; Expert locating systems; Pet recommendation systems; Pet Finding systems; Vet locating systems; Vet Finding Systems.

 

ABSTRACT

ABSTRACT: The Expert Finding Systems are gaining focus in Universities, HR, Medical and Project Management systems. These systems are way ahead of recommendation systems, which are an extensive class of Web applications that involve predicting user responses to options. Expert Finding Systems look beyond the best-fit expert to solve the end user’s problems. These problems can be a query regarding some item, solution, service or trouble shooting some case scenario. This area of research encompasses Artificial Intelligence, Web application engineering and also Software Engineering used to validate the simulated responses of this system. This paper talks about an expert finding system in pet domain. The expertise in Pet Domain can be in form of Doctor, trainer, breeder, groomer or Dog Shelter organizations. The real challenges are to recognize the problem scenario, understand the user, data and the environment, taking care of feasibility and then giving the novice user, the best fit solution for his pet. The goal is requirement analysis for best fit search foraying in array of choices of this ever changing E-world and Recommendation architecture which provides contemporary problems in every search engine and recommendation system’s research. This paper addresses all this and assists the user to find their requirements easily and undermine the information overloading as well as over specialization problems along with other recommendation issues.

 

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