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cs.IR2021
Sequence Adaptation via Reinforcement Learning in Recommender Systems
Stefanos Antaris, Dimitrios Rafailidis
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…
cs.IR2020
Adaptive Neural Architectures for Recommender Systems
Dimitrios Rafailidis, Stefanos Antaris
Deep learning has proved an effective means to capture the non-linear associations of user preferences. However, the main drawback of existing deep learning architectures is that t…