  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.
