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Journal of Applied Sciences
  Year: 2009 | Volume: 9 | Issue: 2 | Page No.: 320-326
DOI: 10.3923/jas.2009.320.326
A New Genetic Algorithm Recommender System for Achieving Customer-Seller Win-Win Quiescent Point
A.A. Niknafs, A. Niknafs and M.E. Shiri

Abstract:
In this study, a new algorithm for considering the benefits of both customer and seller is proposed which is based on a win-win strategy in trade negotiations. This approach causes both sides to achieve a win-win quiescent point. In traditional commerce, this is done by negotiations between seller and customer. In this proposed method the preferences and needs of customer and seller are captured through the user interface. The algorithm compromises these two groups of factors and offers one or more recommendations that are satisfactory to both sides as much as possible. Although the system is designed based on the typical framework of collaborative filtering, yet it considers additional factor to item and customer that is the seller. The genetic algorithm is considered as a useful method for finding the best solutions for this problem. A simple example of e-negotiation between seller and customer is simulated and implemented using C No. and SQL server. The main application of the algorithm is in sophisticated ecommerce projects like tenders and contracts. The experiments results show the feasibility of the system and both customer and seller satisfaction.
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How to cite this article:

A.A. Niknafs, A. Niknafs and M.E. Shiri, 2009. A New Genetic Algorithm Recommender System for Achieving Customer-Seller Win-Win Quiescent Point. Journal of Applied Sciences, 9: 320-326.

DOI: 10.3923/jas.2009.320.326

URL: https://scialert.net/abstract/?doi=jas.2009.320.326

 
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