  Recent recommender systems have started to employ knowledge distillation,
which is a model compression technique distilling knowledge from a cumbersome
model (teacher) to a compact model (student), to reduce inference latency while
maintaining performance. The state-of-the-art methods have only focused on
making the student model accurately imitate the predictions of the teacher
model. They have a limitation in that the prediction results incompletely
reveal the teacher's knowledge. In this paper, we propose a novel knowledge
distillation framework for recommender system, called DE-RRD, which enables the
student model to learn from the latent knowledge encoded in the teacher model
as well as from the teacher's predictions. Concretely, DE-RRD consists of two
methods: 1) Distillation Experts (DE) that directly transfers the latent
knowledge from the teacher model. DE exploits "experts" and a novel expert
selection strategy for effectively distilling the vast teacher's knowledge to
the student with limited capacity. 2) Relaxed Ranking Distillation (RRD) that
transfers the knowledge revealed from the teacher's prediction with
consideration of the relaxed ranking orders among items. Our extensive
experiments show that DE-RRD outperforms the state-of-the-art competitors and
achieves comparable or even better performance to that of the teacher model
with faster inference time.
