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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.
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Neural collaborative filtering. In WWW
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Action Space Learning for Heterogeneous User Behavior Prediction. In IJCAI
Dongha Lee, Chanyoung Park, Hyunjun Ju, Junyoung Hwang, and Hwanjo Yu. 2019b
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Candidate Generation with Binary Codes for Large-Scale Top-N Recommendation. In CIKM
Wang-Cheng Kang and Julian McAuley. 2019 · 2019
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Collaborative Distillation for Top-N Recommendation
Jaewoong Lee, Minjin Choi, Jongwuk Lee, and Hyunjung Shim. 2019a · 2019
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Binarized collaborative filtering with distilling graph convolutional networks
Haoyu Wang, Defu Lian, and Yong Ge. 2019 · 2019
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Deep Rating Elicitation for New Users in Collaborative Filtering. In WWW
Wonbin Kweon, Seongku Kang, Junyoung Hwang, and Hwanjo Yu. 2020 · 2020
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