A recommender system based on genetic algorithm for music data

Nowadays, 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 the items that might interest them. The ability of promptly responding to changes in user´s preference is a valuable asset for such systems. This paper presents an innovative recommender system for music data that combines two methodologies, the content-based filtering technique and the interactive genetic algorithm. The proposed system aims to effectively adapt and respond to immediate changes in users´ preferences. The experiments conducted in an objective manner exhibit that our system is able to recommend items suitable with the subjective favorite of each individual user.