Aggregation of Rankings Using Metaheuristics in Recommendation Systems

Recommendation systems are a powerful tool that is an integral part of great many websites. Most often, recommendations presented in the form list generated by using various recommendation methods. Typically, however, these methods do not generate identical recommendations, and their effectiveness varies between users. In order to solve this problem, application aggregation techniques was suggested, aim which combine several lists into one, which, theory, should improve overall quality recommendations. For reason, we suggest Differential Evolution algorithm, will be aggregate individual algorithms create single fine-tuned user’s preferences. Additionally, based on our previous research, present suggestions speed up process.