# Adaptive Hardness Negative Sampling for Collaborative Filtering

arXiv: https://arxiv.org/abs/2401.05191 · HTML: https://mlsanity.com/paper/2401.05191/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jan 2024.

## Built on by (most cited first, 8 of 8)

- [G-UBS: Towards Robust Understanding of Implicit Feedback via Group-Aware User Behavior Simulation](https://mlsanity.com/paper/2508.05709/) (2025)
- [Towards Reliable Negative Sampling for Recommendation with Implicit Feedback via In-Community Popularity](https://mlsanity.com/paper/2602.18759/) (2026)
- [Causal Negative Sampling via Diffusion Model for Out-of-Distribution Recommendation](https://mlsanity.com/paper/2508.07243/) (2025)
- [IntSR: An Integrated Generative Framework for Search and Recommendation](https://mlsanity.com/paper/2509.21179/) (2025)
- [Towards A Tri-View Diffusion Framework for Recommendation](https://mlsanity.com/paper/2511.20122/) (2025)
- [STARS: Semantic Tokens with Augmented Representations for Recommendation at Scale](https://mlsanity.com/paper/2512.10149/) (2025)
- [TFPS: A Temporal Filtration-enhanced Positive Sample Set Construction Method for Implicit Collaborative Filtering](https://mlsanity.com/paper/2602.22521/) (2026)
- [Soft Curriculum Learning for Optimizing Fresh and Generalized Recommendations](https://mlsanity.com/paper/2609.35783/) (2026)

## Abstract (arXiv)

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<0}, and theoretically demonstrate that AHNS_{p<0} can fit the three criteria of AHNS well and achieve a larger lower bound of normalized discounted cumulative gain. Besides, we note that existing negative sampling methods can be regarded as more relaxed cases of AHNS. Finally, we conduct comprehensive experiments, and the results show that AHNS_{p<0} can consistently and substantially outperform several state-of-the-art competitors on multiple datasets.