An AutoML-driven framework with a modular code base for deep session-based recommendation systems and a built-in component for automated Hyper Parameter Tuning
Amir Reza Mohammadi, Amir Hossein Karimi, Mahdi Bohlouli, Eva Zangerle and GÃƒÂ¼nther Specht
Journal of Intelligent Computing, Vol. 15, No. 3, 2024.
DOI: https://doi.org/10.6025/jic/2024/15/3/89-99
Link: https://www.dline.info/jic/fulltext/v15n3/jicv15n3_2.pdf
Abstract:
Recommendation systems have advanced beyond simple user-item matching techniques in studies. However, these matching techniques are still widely used in practical applications, mainly because they are simpler to troubleshoot and adjust. The current systems, while effective, fall short of supporting the optimization of algorithms. They prioritize the consistency of top-performing accuracy over the simplicity of algorithm creation and upkeep, which can impede quick and repeated testing and fixing. This project introduces an AutoML-driven framework with a modular code base for deep session-based recommendation systems and a built-in component for automated HyperParameter Tuning (HPT4Rec). This framework streamlines the process of finding the optimal session-based model for a specific dataset, enabling the model to be regularly updated as the nature and volume of the data change in real-world settings. Our tests on the RecSys 2015 dataset have shown that HPT4Rec achieves results comparable to the best available. The outcomes of our tests underscore the importance of ongoing and iterative adjustment of parameters, especially in real-world applications, for maintaining high recommendation precision.