Knowledge distillation meets recommendation: collaborative distillation for top-N recommendation

Knowledge distillation (KD) is a successful method for transferring knowledge from one model (i.e., teacher model) to another model (i.e., student model). Despite the success of KD in classification tasks, applying KD to recommender models is challenging because of the sparsity of positive feedback, ambiguity of missing feedback, and ranking problem for top-N recommendation. In this paper, we propose a new KD model for collaborative filtering, namely collaborative distillation (CD). Specifically, (1) we reformulate a loss function to deal with the ambiguity of missing feedback. (2) We exploit probabilistic rank-aware sampling for top-N recommendation. (3) To train the proposed model effectively, we develop two training strategies for the student model, called teacher- and student-guided training methods, adaptively selecting the most beneficial feedback from the teacher model. Furthermore, we extend our model using self-distillation, called born-again CD (BACD). That is, the teacher and student models with the same model capacity are trained by using the proposed distillation method. The experimental results demonstrate that CD outperforms the state-of-the-art method by 2.7-33.2% and 2.7-29.9% in hit rate (HR) and normalized discounted cumulative gain (NDCG), respectively. Moreover, BACD improves the teacher model by 3.5-12.0% and 4.9-13.3% in HR and NDCG, respectively.