Collaborative Rank Aggregation in Recommendation Systems
Michał Bałchanowski, Urszula Boryczka · Procedia Computer Science · 2022
Over the years, various techniques of generating recommendations have been developed. However, it turns out that when we compare the recommendations generated by different algorithms in the context of a particular user, the quality of such recommendations for different techniques may differ. The use of the aggregation techniques, the aim of which is to combine several rankings into one, can be a solution to this problem. In theory it should improve the quality of the recommendations. Additionally, in order to personalize the recommendations better, a metaheuristic algorithm, which, by assigning different weights to each feature, tries to represent the preference of the active user, was used. This paper also presents a suggestion to include additional rankings generated for other users in the system in the aggregation process. The idea will be supported by research results that clearly show that taking into account rankings of other users can improve the quality of the generated recommendations.