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 code is made available at https://github.com/otto-de/MultiTRON . The results confirm the model's ability to manage multiple recommendation objectives effectively, offering a flexible tool for diverse business needs.

翻译：本研究提出了一种名为MultiTRON的方法，该方法将帕累托前沿逼近技术应用于基于会话的多目标推荐系统，并采用Transformer神经网络实现。我们的方法通过对采样的偏好向量进行训练，优化了点击率与转化率等关键指标之间的权衡关系。一个显著优势在于：训练完成后，单个模型即可覆盖整个帕累托前沿，通过调整用于加权目标函数的附加输入向量，能够根据不同利益相关方的具体需求进行定制化适配。我们通过大量离线与在线评估验证了模型的性能。为促进更广泛的应用与研究，源代码已在https://github.com/otto-de/MultiTRON公开。实验结果证实了该模型能有效管理多个推荐目标，为多样化的业务需求提供了灵活的工具。