Pareto Front Approximation for Multi-Objective Session-Based Recommender Systems
Pages 809 - 812
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.
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Index Terms
- Pareto Front Approximation for Multi-Objective Session-Based Recommender Systems
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Published In
October 2024
1438 pages
ISBN:9798400705052
DOI:10.1145/3640457
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- SIGCHI: ACM Special Interest Group on Computer-Human Interaction
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Published: 08 October 2024
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