A novel Enhanced Collaborative Autoencoder with knowledge distillation for top-N recommender systems
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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
- ORCID
- 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
About these checks
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.
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