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 a bility of promptly responding to changes in user's perspective 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 interact ive genetic algorithm. The proposed system aims to effectively adapt and respond to immediate changes in user’s preferences. The experiments conducted in an objective manner exhibit that our system is able to recommend items suitable with the subjective fa vorite of each individual user.
Github Link : https://github.com/girish-rajiv-patil/MRS-MUSIC-RECOMMENDATION-SYSTEM download report here