ADER: Adaptively Distilled Exemplar Replay Towards Continual Learning for Session-based Recommendation

By Fei Mi, Xiao Lin, Boi Faltings. 2020

68 citations recorded by the source corpus.

Session-based recommendation has received growing attention recently due to the increasing privacy concern. Despite the recent success of neural session-based recommenders, they are typically developed in an offline manner using a static dataset. However, recommendation requires continual adaptation to take into account new and obsolete items and users, and requires “continual learning” in real-life applications. In this case, the recommender is updated continually and periodically with new data that arrives in each update cycle, and the updated model needs to provide recommendations for user activities before the next model update. A major challenge for continual learning with neural models is catastrophic forgetting, in which a continually trained model forgets user preference patterns it has learned before. To deal with this challenge, we propose a method called Adaptively Distilled Exemplar Replay (ADER) by periodically replaying previous training samples (i.e., exemplars) to the current model with an adaptive distillation loss. Experiments are conducted based on the state-of-the-art SASRec model using two widely used datasets to benchmark ADER with several well-known continual learning techniques. We empirically demonstrate that ADER consistently outperforms other baselines, and it even outperforms the method using all historical data at every update cycle. This result reveals that ADER is a promising solution to mitigate the catastrophic forgetting issue towards building more realistic and scalable session-based recommenders.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

Online Continual Learning in Image Classification: An Empirical Survey (2021, methodology): “ADER [154] is proposed to handle CF in session-based recommendation using the adaptive distillation loss and replay with heading [90] technique.”
Model-Free Generative Replay for Lifelong Reinforcement Learning: Application to Starcraft-2 (2022, background): “These are accumulated over the lifetime of the agent from all wake replay buffers bw (although a more sophisticated exemplar selection (Rebuffi et al., 2017; Mi et al., 2020) is also possible).”
One Person, One Model, One World: Learning Continual User Representation without Forgetting (2020, background): “Meanwhile, there are also some recent work in [23, 29, 31] claiming that RS models should have the so-called ‘lifelong’ learning capacity.”