1 research outputs found
Aggregation of Rankings Using Metaheuristics in Recommendation Systems
Recommendation systems are a powerful tool that is an integral part of a great many
websites. Most often, recommendations are presented in the form of a list that is 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, the
application of aggregation techniques was suggested, the aim of which is to combine several lists
into one, which, in theory, should improve the overall quality of the generated recommendations.
For this reason, we suggest using the Differential Evolution algorithm, the aim of which will be to
aggregate individual lists generated by the recommendation algorithms and to create a single list
that will be fine-tuned to the user’s preferences. Additionally, based on our previous research, we
present suggestions to speed up this process