mlsanityData to 7 Oct 2026

PINA: Leveraging Side Information in eXtreme Multi-label Classification via Predicted Instance Neighborhood Aggregation

arXiv 2305.12349 ↗First posted 21 May 2023cs.LG cs.IRcited by 7

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Abstract (arXiv)

The eXtreme Multi-label Classification~(XMC) problem seeks to find relevant labels from an exceptionally large label space. Most of the existing XMC learners focus on the extraction of semantic features from input query text. However, conventional XMC studies usually neglect the side information of instances and labels, which can be of use in many real-world applications such as recommendation systems and e-commerce product search. We propose Predicted Instance Neighborhood Aggregation (PINA), a data enhancement method for the general XMC problem that leverages beneficial side information. Unlike most existing XMC frameworks that treat labels and input instances as featureless indicators and independent entries, PINA extracts information from the label metadata and the correlations among training instances. Extensive experimental results demonstrate the consistent gain of PINA on various XMC tasks compared to the state-of-the-art methods: PINA offers a gain in accuracy compared to standard XR-Transformers on five public benchmark datasets. Moreover, PINA achieves a ~ 5% gain in accuracy on the largest dataset LF-AmazonTitles-1.3M. Our implementation is publicly available.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 41 of its 66 references that are in the map. First posted on arXiv 21 May 2023.

Builds on

41 of 66 references.

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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 3; 2025: 1; 2026: 1.

7 papers.

  1. 2023Generalized test utilities for long-tail performance in extreme multi-label classificationcited by 6
  2. 2023Dense Retrieval as Indirect Supervision for Large-space Decision Makingcited by 3
  3. 2024Learning label-label correlations in Extreme Multi-label Classification via Label Featurescited by 3
  4. 2025Large Language Models Meet Extreme Multi-label Classification: Scaling and Multi-modal Frameworkcited by 3
  5. 2024Prototypical Extreme Multi-label Classification with a Dynamic Margin Losscited by 2
  6. 2024Graph Regularized Encoder Training for Extreme Classificationcited by 1
  7. 2026LLM-Enhanced Dual-Branch Learning for Large-Scale Multi-Label Text Classificationcited by 0