In recent years, Multi-task Learning (MTL) has yielded immense success in Recommender System (RS) applications. However, current MTL-based recommendation models tend to disregard the session-wise patterns of user-item interactions because they are predominantly constructed based on item-wise datasets. Moreover, balancing multiple objectives has always been a challenge in this field, which is typically avoided via linear estimations in existing works. To address these issues, in this paper, we propose a Reinforcement Learning (RL) enhanced MTL framework, namely RMTL, to combine the losses of different recommendation tasks using dynamic weights. To be specific, the RMTL structure can address the two aforementioned issues by (i) constructing an MTL environment from session-wise interactions and (ii) training multi-task actor-critic network structure, which is compatible with most existing MTL-based recommendation models, and (iii) optimizing and fine-tuning the MTL loss function using the weights generated by critic networks. Experiments on two real-world public datasets demonstrate the effectiveness of RMTL with a higher AUC against state-of-the-art MTL-based recommendation models. Additionally, we evaluate and validate RMTL's compatibility and transferability across various MTL models.

翻译：近年来，多任务学习（MTL）在推荐系统（RS）应用中取得了巨大成功。然而，当前基于MTL的推荐模型倾向于忽略用户-物品交互的会话级模式，因为它们主要基于物品级数据集构建。此外，平衡多个目标一直是该领域的挑战，现有工作通常通过线性估计来规避这一问题。为解决上述问题，本文提出一种强化学习（RL）增强的MTL框架，即RMTL，通过动态权重组合不同推荐任务的损失。具体而言，RMTL结构可通过以下方式解决上述两个问题：（i）从会话级交互构建MTL环境；（ii）训练与大多数现有基于MTL的推荐模型兼容的多任务演员-评论家网络结构；（iii）利用评论家网络生成的权重优化和微调MTL损失函数。在两个真实世界公开数据集上的实验表明，与最先进的基于MTL的推荐模型相比，RMTL具有更高的AUC，从而验证了其有效性。此外，我们评估并验证了RMTL在各种MTL模型上的兼容性和可迁移性。