Cited by 97Recommender Systems and TechniquesInformation Retrieval and Search BehaviorAdvanced Bandit Algorithms Research

A novel Enhanced Collaborative Autoencoder with knowledge distillation for top-N recommender systems

Pan, Yiteng, He, Fazhi, Yu, Haiping·2019·Neurocomputing

Integrity signals

Registry

Crossref · 10.1016/j.neucom.2018.12.025

Cites

58 of 58 references verified0 citation errors · 0 not checkable

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  • Knowledge distillation has been successfully applied in recommender systems for deep model compression and acceleration.

    “Recently, knowledge distillation has been successfully applied in recommender systems for deep model compression and acceleration (Chen et al., 2018b; Tang and Wang, 2018; Pan et al., 2019).”

    From Knowledge Distillation: A Survey (2021) · cites this paper as Pan et al., 2019

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Authors
Pan, Y., He, F., & Yu, H.
Affiliations
Wuhan University (CN)
Corresponding author
He, Fazhi
Venue
Neurocomputing
Publisher
Elsevier BV
Published
2019-03
Volume
332
Pages
137-148
ISSN
0925-2312 (ISSN-L)
OpenAlex
W2908414126
Cited by
97
References
61
Citations by year
2026: 2 · 2025: 6 · 2024: 5 · 2023: 15 · 2022: 18 · 2021: 19 · 2020: 16 · 2019: 16
FWCI
19.67
Type
journal article
Language
en
Open access
closed
Locations
Neurocomputing (publishedVersion)
Funders
National Science Foundation of China (61472289)
Topics
  • Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
  • Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Advanced Bandit Algorithms Research
Keywords
top-N recommendation, knowledge distillation, user interest modeling, recommender systems

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

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