Year: 2024
Type: conference-paper
Abstract: This work introduces MultiTRON, an approach that adapts Pareto front approximation techniques to multi-objective session-based recommender systems using a transformer neural network. Our approach optimizes trade-offs between key metrics such as click-through and conversion rates by training on sampled preference vectors. A significant advantage is that after training, a single model can access the entire Pareto front, allowing it to be tailored to meet the specific requirements of different stakeholders by adjusting an additional input vector that weights the objectives. We validate the model’s performance through extensive offline and online evaluation. For broader application and research, the source code1 is made available. The results confirm the model’s ability to manage multiple recommendation objectives effectively, offering a flexible tool for diverse business needs.
Authors: Timo Wilm, Philipp Normann, Felix Stepprath
Institution: Otto Group (Germany)
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4403219555