mlsanityData to 7 Oct 2026

Multi-Sample based Contrastive Loss for Top-k Recommendation

arXiv 2109.00217 ↗First posted 1 Sep 2021cs.IR cs.AIcited by 7

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Abstract (arXiv)

The top-k recommendation is a fundamental task in recommendation systems which is generally learned by comparing positive and negative pairs. The Contrastive Loss (CL) is the key in contrastive learning that has received more attention recently and we find it is well suited for top-k recommendations. However, it is a problem that CL treats the importance of the positive and negative samples as the same. On the one hand, CL faces the imbalance problem of one positive sample and many negative samples. On the other hand, positive items are so few in sparser datasets that their importance should be emphasized. Moreover, the other important issue is that the sparse positive items are still not sufficiently utilized in recommendations. So we propose a new data augmentation method by using multiple positive items (or samples) simultaneously with the CL loss function. Therefore, we propose a Multi-Sample based Contrastive Loss (MSCL) function which solves the two problems by balancing the importance of positive and negative samples and data augmentation. And based on the graph convolution network (GCN) method, experimental results demonstrate the state-of-the-art performance of MSCL. The proposed MSCL is simple and can be applied in many methods. We will release our code on GitHub upon the acceptance.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 33 of its 66 references that are in the map. First posted on arXiv 1 Sep 2021.

Builds on

33 of 66 references.

References · 33

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21 more
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  2. 2019Neural Graph Collaborative Filteringcited by 660
  3. 2020Contrastive Multi-View Representation Learning on Graphscited by 651
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Built on by

Cited by year

Citing papers per year: 2022: 1; 2023: 3; 2024: 2; 2025: 1.

7 papers.

  1. 2022Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learningcited by 86
  2. 2023Adap-τ: Adaptively Modulating Embedding Magnitude for Recommendationcited by 19
  3. 2024A Survey on Self-Supervised Graph Foundation Models: Knowledge-Based Perspectivecited by 9
  4. 2024Guidelines for Augmentation Selection in Contrastive Learning for Time Series Classificationcited by 5
  5. 2023Graph-based Alignment and Uniformity for Recommendationcited by 4
  6. 2023A Comparative Analysis of Bias Amplification in Graph Neural Network Approaches for Recommender Systemscited by 0
  7. 2025Context-Enriched Contrastive Loss: Enhancing Presentation of Inherent Sample Connections in Contrastive Learning Frameworkcited by 0