Sequence Adaptation via Reinforcement Learning in Recommender Systems

@article{Antaris2021SequenceAV,
  title={Sequence Adaptation via Reinforcement Learning in Recommender Systems},
  author={Stefanos Antaris and Dimitrios Rafailidis},
  journal={Proceedings of the 15th ACM Conference on Recommender Systems},
  year={2021}
}
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… 

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