  Accounting for the fact that users have different sequential patterns, the
main drawback of state-of-the-art recommendation strategies is that a fixed
sequence length of user-item interactions is required as input to train the
models. This might limit the recommendation accuracy, as in practice users
follow different trends on the sequential recommendations. Hence, baseline
strategies might ignore important sequential interactions or add noise to the
models with redundant interactions, depending on the variety of users'
sequential behaviours. To overcome this problem, in this study we propose the
SAR model, which not only learns the sequential patterns but also adjusts the
sequence length of user-item interactions in a personalized manner. We first
design an actor-critic framework, where the RL agent tries to compute the
optimal sequence length as an action, given the user's state representation at
a certain time step. In addition, we optimize a joint loss function to align
the accuracy of the sequential recommendations with the expected cumulative
rewards of the critic network, while at the same time we adapt the sequence
length with the actor network in a personalized manner. Our experimental
evaluation on four real-world datasets demonstrates the superiority of our
proposed model over several baseline approaches. Finally, we make our
implementation publicly available at https://github.com/stefanosantaris/sar.
