Abstract
Recommendation systems are an effective solution for personalising e-commerce services. They are able to provide customers with relevant and useful products. Their performance is determined by the quality of the methods employed. However, it is also influenced by the input data. Session-based (SB) techniques are highly effective in real-world scenario to generating recommendations that focus on short-term user activities. This study aims to investigate the relation between data statistics and performance of SB algorithms measured by accuracy and coverage.
Paper Type
Poster
DOI
10.62036/ISD.2024.24
Characteristics of the Learning Data of a Session-Based Recommendation System and their Impact on the Performance of the System
Recommendation systems are an effective solution for personalising e-commerce services. They are able to provide customers with relevant and useful products. Their performance is determined by the quality of the methods employed. However, it is also influenced by the input data. Session-based (SB) techniques are highly effective in real-world scenario to generating recommendations that focus on short-term user activities. This study aims to investigate the relation between data statistics and performance of SB algorithms measured by accuracy and coverage.
Recommended Citation
Kużelewska, U. & Charytanowicz, M. (2024). Characteristics of the Learning Data of a Session-Based Recommendation System and their Impact on the Performance of the System. In B. Marcinkowski, A. Przybylek, A. Jarzębowicz, N. Iivari, E. Insfran, M. Lang, H. Linger, & C. Schneider (Eds.), Harnessing Opportunities: Reshaping ISD in the post-COVID-19 and Generative AI Era (ISD2024 Proceedings). Gdańsk, Poland: University of Gdańsk. ISBN: 978-83-972632-0-8. https://doi.org/10.62036/ISD.2024.24