A recommender system based on interactive evolutionary computation with data grouping

Nowadays, recommender systems are widely applied in e-commerce websites to help customers in finding the items they want. A recommender system should be able to provide users with useful information about the items that might be interesting to them. The ability of immediately responding to changes in users preferences is a valuable asset for such systems. This paper presents a novel recommender system that combines two methodologies, interactive evolutionary computation and content-based filtering method. Also, the proposed system applies clustering to increase the time efficiency. The system aims to effectively adapt and respond to immediate changes in users preference. The experiments conducted in an objective manner exhibit that the proposed system is able to make recommendation with ensuring quality and speed.