# Hyper-Class Representation of Data

arXiv: https://arxiv.org/abs/2201.13317 · HTML: https://mlsanity.com/paper/2201.13317/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jan 2022.

## Built on by (most cited first, 7 of 7)

- [Mitigating Propensity Bias of Large Language Models for Recommender Systems](https://mlsanity.com/paper/2409.20052/) (2024)
- [Structural-Entropy-Based Sample Selection for Efficient and Effective Learning](https://mlsanity.com/paper/2410.02268/) (2024)
- [Efficient Adaptive Label Refinement for Label Noise Learning](https://mlsanity.com/paper/2502.00386/) (2025)
- [Trustworthiness-Driven Graph Convolutional Networks for Signed Network Embedding](https://mlsanity.com/paper/2309.00816/) (2023)
- [Interaction-Data-guided Conditional Instrumental Variables for Debiasing Recommender Systems](https://mlsanity.com/paper/2408.09651/) (2024)
- [Toward Fair Graph Neural Networks Via Dual-Teacher Knowledge Distillation](https://mlsanity.com/paper/2412.00382/) (2024)
- [Hashing Learning with Hyper-Class Representation](https://mlsanity.com/paper/2206.02334/) (2022)

## Abstract (arXiv)

Data representation is usually a natural form with their attribute values. On this basis, data processing is an attribute-centered calculation. However, there are three limitations in the attribute-centered calculation, saying, inflexible calculation, preference computation, and unsatisfactory output. To attempt the issues, a new data representation, named as hyper-classes representation, is proposed for improving recommendation. First, the cross entropy, KL divergence and JS divergence of features in data are defined. And then, the hyper-classes in data can be discovered with these three parameters. Finally, a kind of recommendation algorithm is used to evaluate the proposed hyper-class representation of data, and shows that the hyper-class representation is able to provide truly useful reference information for recommendation systems and makes recommendations much better than existing algorithms, i.e., this approach is efficient and promising.