# DISCO: A Hierarchical Disentangled Cognitive Diagnosis Framework for Interpretable Job Recommendation

arXiv: https://arxiv.org/abs/2410.07671 · HTML: https://mlsanity.com/paper/2410.07671/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 55 references that are in the map.
- First posted on arXiv 10 Oct 2024.

## Builds on (most cited first, 25 of 25)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation](https://mlsanity.com/paper/2002.02126/) (2020)
- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [Neural Graph Collaborative Filtering](https://mlsanity.com/paper/1905.08108/) (2019)
- [AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks](https://mlsanity.com/paper/1810.11921/) (2018)
- [Are Graph Augmentations Necessary? Simple Graph Contrastive Learning for Recommendation](https://mlsanity.com/paper/2112.08679/) (2021)
- [Disentangled Representation Learning](https://mlsanity.com/paper/2211.11695/) (2022)
- [Learning Disentangled Representations for Recommendation](https://mlsanity.com/paper/1910.14238/) (2019)
- [Neural Cognitive Diagnosis for Intelligent Education Systems](https://mlsanity.com/paper/1908.08733/) (2019)
- [Instructions and Guide for Diagnostic Questions: The NeurIPS 2020 Education Challenge](https://mlsanity.com/paper/2007.12061/) (2020)
- [Enhancing Person-Job Fit for Talent Recruitment: An Ability-aware Neural Network Approach](https://mlsanity.com/paper/1812.08947/) (2018)
- [Disentangled Contrastive Collaborative Filtering](https://mlsanity.com/paper/2305.02759/) (2023)
- [Modeling Two-Way Selection Preference for Person-Job Fit](https://mlsanity.com/paper/2208.08612/) (2022)
- [A Comprehensive Survey of Artificial Intelligence Techniques for Talent Analytics](https://mlsanity.com/paper/2307.03195/) (2023)
- [Learning Effective Representations for Person-Job Fit by Feature Fusion](https://mlsanity.com/paper/2006.07017/) (2020)
- [SetRank: A Setwise Bayesian Approach for Collaborative Ranking from Implicit Feedback](https://mlsanity.com/paper/2002.09841/) (2020)
- [Endowing Interpretability for Neural Cognitive Diagnosis by Efficient Kolmogorov-Arnold Networks](https://mlsanity.com/paper/2405.14399/) (2024)
- [ReliCD: A Reliable Cognitive Diagnosis Framework with Confidence Awareness](https://mlsanity.com/paper/2401.10749/) (2023)
- [Person-Job Fit: Adapting the Right Talent for the Right Job with Joint Representation Learning](https://mlsanity.com/paper/1810.04040/) (2018)
- [RIGL: A Unified Reciprocal Approach for Tracing the Independent and Group Learning Processes](https://mlsanity.com/paper/2406.12465/) (2024)
- [Leveraging Search History for Improving Person-Job Fit](https://mlsanity.com/paper/2203.14232/) (2022)
- [Evolutionary Neural Architecture Search for Transformer in Knowledge Tracing](https://mlsanity.com/paper/2310.01180/) (2023)
- [Towards Efficient Resume Understanding: A Multi-Granularity Multi-Modal Pre-Training Approach](https://mlsanity.com/paper/2404.13067/) (2024)
- [Job-SDF: A Multi-Granularity Dataset for Job Skill Demand Forecasting and Benchmarking](https://mlsanity.com/paper/2406.11920/) (2024)
- [Towards Explainable Collaborative Filtering with Taste Clusters Learning](https://mlsanity.com/paper/2304.13937/) (2023)

## Built on by (most cited first, 2 of 2)

- [Diffusion-Inspired Cold Start with Sufficient Prior in Computerized Adaptive Testing](https://mlsanity.com/paper/2411.12182/) (2024)
- [A Unified LLM-Adaptable Framework for Cold-Start Cognitive Diagnosis](https://mlsanity.com/paper/2505.21239/) (2025)

## Abstract (arXiv)

The rapid development of online recruitment platforms has created unprecedented opportunities for job seekers while concurrently posing the significant challenge of quickly and accurately pinpointing positions that align with their skills and preferences. Job recommendation systems have significantly alleviated the extensive search burden for job seekers by optimizing user engagement metrics, such as clicks and applications, thus achieving notable success. In recent years, a substantial amount of research has been devoted to developing effective job recommendation models, primarily focusing on text-matching based and behavior modeling based methods. While these approaches have realized impressive outcomes, it is imperative to note that research on the explainability of recruitment recommendations remains profoundly unexplored. To this end, in this paper, we propose DISCO, a hierarchical Disentanglement based Cognitive diagnosis framework, aimed at flexibly accommodating the underlying representation learning model for effective and interpretable job recommendations. Specifically, we first design a hierarchical representation disentangling module to explicitly mine the hierarchical skill-related factors implied in hidden representations of job seekers and jobs. Subsequently, we propose level-aware association modeling to enhance information communication and robust representation learning both inter- and intra-level, which consists of the interlevel knowledge influence module and the level-wise contrastive learning. Finally, we devise an interaction diagnosis module incorporating a neural diagnosis function for effectively modeling the multi-level recruitment interaction process between job seekers and jobs, which introduces the cognitive measurement theory.