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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 Synthesis Of Recommendation Systems - Finding Expert Recommendations For Cuisines

[Full Text]

 

AUTHOR(S)

Minakshi Gujral, Satish Chandra

 

KEYWORDS

Keywords : Expert finding systems; Knowledge engineering; Recommendation systems; Information Systems, Personalized ratings; Information Overload; ratings; personalized recommendations; Expert locating systems.

 

ABSTRACT

ABSTRACT: Knowledge discovery tools and techniques are used in an increasing number of scientific and commercial areas. They further augment the analysis and knowledge processing of voluminous Information. Expert finding systems are a web enabled Knowledge Discovery from databases frameworks. Lot has been talked about effective, accurate and well balanced expert recommendations but many shortcomings of the proposed solutions have come into picture. In this Paper we try to elucidate and model Expert Recommendation issues from multidimensional, multi-criteria and real world’s perspectives by evaluating some selected systems. Another aspect of expert recommendation prototype introduced in this paper is personalization. Due to information overload and other issues of recommendations, an internet user feels it difficult to search the expert information relevant to them. Local search is yet another field that faces this problem due to unavailability of expert, maturation effect of environment and changing patterns of user likings and Interest. This work portrays a personalized expert recommendation system which takes into account the profile attributes of a user and recommends results that are highly rated by other users of similar profile. The introduced method does not depend only on the ratings given by the user as a feedback but it also considers various other parameters which increase accuracy of recommendations. This prevents malicious results to be highly rated and recommended.

 

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