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Research Article Open Access
Recommender systems are widely implemented in E-commerce websites to assist customers in finding the items they need. A recommender system should also be able to provide users with useful information about that might interest them. The ability of promptly responding to changes in user’s preference is a valuable asset for such systems. The recommender system presents an innovative recommender system for music data that combines two methodologies; the content based filtering technique and the interactive genetic algorithm. The recommender system analyzes and recommends items that are appropriate with their own favorites. The recommender system provides recommendation by collecting user’s profiles and discovers relations between each profile. Today increasing numbers of people are turning to computational recommendersystems. Emerging in response to the technological possibilities and human needs created by the World Wide Web, these systems aim to mediate, support, or automate the everyday process of sharing recommendations. The main goal is to identify challenges and suggest new opportunities.