How Normalization Strategies Affect the Quality of Rank Aggregation Methods in Recommendation Systems
Michał Bałchanowski, Urszula Boryczka · Procedia Computer Science · 2023
Many recommendation algorithms have been proposed in the literature that generates personalized recommendations. These recommendations are often presented to the user as an ordered list of suggested items (so-called top-N recommendation). However, despite years of research, no algorithm has been proposed to generate high-quality recommendations for all users in the system. To improve the quality of the final recommendation, aggregation techniques may be employed to combine the results returned by several recommendation algorithms. However, prior to the aggregation process, various normalization techniques are often utilized. This paper will present the results of experiments designed to investigate how different normalization strategies affect the quality of the created aggregation. The research was conducted using four such strategies and ten unsupervised aggregation methods on the publicly available MovieLens 100k dataset. Results were validated by statistical tests.