  Sequential recommender systems (SRS) have become a research hotspot due to
its power in modeling user dynamic interests and sequential behavioral
patterns. To maximize model expressive ability, a default choice is to apply a
larger and deeper network architecture, which, however, often brings high
network latency when generating online recommendations. Naturally, we argue
that compressing the heavy recommendation models into middle- or light- weight
neural networks is of great importance for practical production systems. To
realize such a goal, we propose AdaRec, a knowledge distillation (KD) framework
which compresses knowledge of a teacher model into a student model adaptively
according to its recommendation scene by using differentiable Neural
Architecture Search (NAS). Specifically, we introduce a target-oriented
distillation loss to guide the structure search process for finding the student
network architecture, and a cost-sensitive loss as constraints for model size,
which achieves a superior trade-off between recommendation effectiveness and
efficiency. In addition, we leverage Earth Mover's Distance (EMD) to realize
many-to-many layer mapping during knowledge distillation, which enables each
intermediate student layer to learn from other intermediate teacher layers
adaptively. Extensive experiments on real-world recommendation datasets
demonstrate that our model achieves competitive or better accuracy with notable
inference speedup comparing to strong counterparts, while discovering diverse
neural architectures for sequential recommender models under different
recommendation scenes.
