Collaborative Distillation for TopN Recommendation Knowledge distillation (KD) is a wellknown method to reduce inference latency by compressing a cumbersome teacher model to a small student model. Despite the success of KD in the classification task, applying KD to recommender models is challenging due to the sparsity of positive feedback, the ambiguity of missing feedback, and the ranking problem associated with the topN recommendation. To address the issues, we propose a new KD model for the collaborative filtering approach, namely collaborative distillation (CD). Specifically, (1) we reformulate a loss function to deal with the ambiguity of missing feedback. (2) We exploit probabilistic rankaware sampling for the topN recommendation. (3) To train the proposed model effectively, we develop two training strategies for the student model, called the teacherand the studentguided training methods, selecting the most useful feedback from the teacher model. Via experimental results, we demonstrate that the proposed model outperforms the stateoftheart method by 5.529.7% and 4.827.8% in hit rate (HR) and normalized discounted cumulative gain (NDCG), respectively. Moreover, the proposed model achieves the performance comparable to the teacher model.

Actively SemiSupervised Collaborative Filtering Collaborative filtering (CF) has been widely used in various recommender systems, but often suffers from the problem of data sparsity which dramatically degrades the recommendation performance. In this paper, we propose a cotraining style semisupervised CF approach towards the task of rating prediction, which exploits a few observed ratings in conjunction with copious unobserved ones to reduce sparsity. In each round of cotraining iterations, our approach utilizes two different neighborhoodbased recommenders, each of which labels the unobserved data for the other recommender; in particular, the most informative unobserved examples are actively selected for labeling, and then the labeling confidence is estimated through validating the influence of the labeling of unobserved examples on the observed ones. Experiments results on the three datasets demonstrate that our approach can effectively exploit unobserved data to improve CF predictions, and achieves better performance than other counterparts.