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Recommender Systems for E-commerce: In Perspective of Business Strategies

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The rapid growth of emerging technologies and Internet has moved the world towards an e-world where most of the things are digitized and available on a mouse click. Most of the commercial transactions are performed on Internet with the help of on-line shopping. The huge amount of data puts an extra overload to the user in performing on-line task. Recommender Systems are being used widely to reduce this extra overload and recommend the scrutinized product to the customers. Several data mining techniques are frequently being used for recommendation technology to enhance the online business, amongst which Collaborative filtering, Association rules and web mining are on top . In this paper we try to give an overview of these recommendation techniques with suitable examples and illustrative diagrams, and change of trends in recommender systems. Also the SWOT analysis is discussed for these technologies that give an idea about the respective effects of these systems on business strategies. Various diagrammatic representations are illustrated. And finally we conclude that there is a need of an extra effort to overcome the limitations in existing technology also the aspects of these technology to be used as a business strategy are discussed.

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