75 citations · 136 across the 3 of their papers we have counts for
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cs.LG2018
Ranking Distillation: Learning Compact Ranking Models With High Performance for Recommender System
Jiaxi Tang, Ke Wang
We propose a novel way to train ranking models, such as recommender systems, that are both effective and efficient. Knowledge distillation (KD) was shown to be successful in image…
cs.IR2018
Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding
Jiaxi Tang, Ke Wang
Top- sequential recommendation models each user as a sequence of items interacted in the past and aims to predict top- ranked items that a user will likely interact in a `nea…