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In the last decade electronic and wireless technologies have changed the way companies do business forever. E-commerce (electronic commerce) and e-business (electronic business) feature as extremely dynamic economic sectors and at the same time, as the most appealing ways of beginning or expanding a business activity. Successful companies today recognize these technologies and the Internet as mainstream to business success. Indeed, their future will continue to be promising to companies seeking means for cost cutting, enhanced productivity, improved efficiency, and increased customers’ satisfaction. On the other hand, this networked economy is notably characterized by the impersonal nature of the online environment and the extensive use of IT (information technology), as opposed to face-to-face contact for transactions.
Recommendation systems have been used in e-commerce sites to make product recommendations and to provide customers with information that helps them decide which product to buy. They are based on different methods and techniques for suggesting products with the most well known being collaborative and content-based filtering. Recently, several recommendation systems adopted hybrid approaches by combining collaborative and content-based features as well as other techniques in order to avoid their limitations. In this chapter, we investigate hybrid recommendations systems and especially the way they support movie e-shops in their attempt to suggest movies to customers. Specifically, we introduce an approach where the knowledge about customers and movies is extracted from usage mining and ontological data in conjunction with customer-movie ratings and matching techniques between customers. This integration provides additional knowledge about customers’ preferences and allows the production of successful recommendations. Even in the case of the cold-start problem where no initial behavioural information is available, the approach can provide logical and relevant recommendations to the customers. The provided recommendations are expected to have higher accuracy in matching customers’ preferences and thus higher acceptance by them. Finally, we describe future trends and challenges and discuss the open issues in the field.
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