Augmented Negative Sampling for Collaborative Filtering

Yuhan Zhao, Rui Chen*, Riwei Lai, Qilong Han*, Hongtao Song, Li Chen

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

Abstract

Negative sampling is essential for implicit-feedback-based collaborative filtering, which is used to constitute negative signals from massive unlabeled data to guide supervised learning. The state-of-the-art idea is to utilize hard negative samples that carry more useful information to form a better decision boundary. To balance efficiency and effectiveness, the vast majority of existing methods follow the two-pass approach, in which the first pass samples a fixed number of unobserved items by a simple static distribution and then the second pass selects the final negative items using a more sophisticated negative sampling strategy. However, selecting negative samples from the original items in a dataset is inherently restricted due to the limited available choices, and thus may not be able to contrast positive samples well. In this paper, we confirm this observation via carefully designed experiments and introduce two major limitations of existing solutions: ambiguous trap and information discrimination. Our response to such limitations is to introduce "augmented"negative samples that may not exist in the original dataset. This direction renders a substantial technical challenge because constructing unconstrained negative samples may introduce excessive noise that eventually distorts the decision boundary. To this end, we introduce a novel generic augmented negative sampling (ANS) paradigm and provide a concrete instantiation. First, we disentangle hard and easy factors of negative items. Next, we generate new candidate negative samples by augmenting only the easy factors in a regulated manner: the direction and magnitude of the augmentation are carefully calibrated. Finally, we design an advanced negative sampling strategy to identify the final augmented negative samples, which considers not only the score function used in existing methods but also a new metric called augmentation gain. Extensive experiments on real-world datasets demonstrate that our method significantly outperforms state-of-the-art baselines. Our code is publicly available at https://github.com/Asa9aoTK/ANS-Recbole.

Original languageEnglish
Title of host publicationProceedings of the 17th ACM Conference on Recommender Systems, RecSys 2023
PublisherAssociation for Computing Machinery (ACM)
Pages256-266
Number of pages11
ISBN (Print)9798400702419
DOIs
Publication statusPublished - 14 Sept 2023
Event17th ACM Conference on Recommender Systems, RecSys 2023 - , Singapore
Duration: 18 Sept 202322 Sept 2023
https://dl.acm.org/doi/proceedings/10.1145/3604915

Publication series

NameProceedings of the ACM Conference on Recommender Systems, RecSys

Conference

Conference17th ACM Conference on Recommender Systems, RecSys 2023
Country/TerritorySingapore
Period18/09/2322/09/23
Internet address

Scopus Subject Areas

  • Computer Science Applications
  • Information Systems
  • Software
  • Control and Systems Engineering

User-Defined Keywords

  • augmented negative sampling
  • collaborative filtering
  • disentanglement learning

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