ML Anthology

Adaptive Hardness Negative Sampling for Collaborative Filtering

Riwei Lai, Rui Chen, Qilong Han, Chi Zhang, Li Chen
AAAI 2024 pp. 8645-8652
doi:10.1609/AAAI.V38I8.28709 /aaai/2024/lai2024aaai-adaptive/

Abstract

Negative sampling is essential for implicit collaborative filtering to provide proper negative training signals so as to achieve desirable performance. We experimentally unveil a common limitation of all existing negative sampling methods that they can only select negative samples of a fixed hardness level, leading to the false positive problem (FPP) and false negative problem (FNP). We then propose a new paradigm called adaptive hardness negative sampling (AHNS) and discuss its three key criteria. By adaptively selecting negative samples with appropriate hardnesses during the training process, AHNS can well mitigate the impacts of FPP and FNP. Next, we present a concrete instantiation of AHNS called AHNS_{p

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Cite

Text

Lai et al. "Adaptive Hardness Negative Sampling for Collaborative Filtering." AAAI Conference on Artificial Intelligence, 2024. doi:10.1609/AAAI.V38I8.28709

Markdown

[Lai et al. "Adaptive Hardness Negative Sampling for Collaborative Filtering." AAAI Conference on Artificial Intelligence, 2024.](https://mlanthology.org/aaai/2024/lai2024aaai-adaptive/) doi:10.1609/AAAI.V38I8.28709

BibTeX

@inproceedings{lai2024aaai-adaptive,
  title     = {{Adaptive Hardness Negative Sampling for Collaborative Filtering}},
  author    = {Lai, Riwei and Chen, Rui and Han, Qilong and Zhang, Chi and Chen, Li},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2024},
  pages     = {8645-8652},
  doi       = {10.1609/AAAI.V38I8.28709},
  url       = {https://mlanthology.org/aaai/2024/lai2024aaai-adaptive/}
}