arXiv subjects

Xiyuan Zhu

Publications and source records attributed to Xiyuan Zhu.

3 recordsLinked to original sources

arXiv2026

RecruitScope: A Visual Analytics System for Multidimensional Recruitment Data Analysis

Online recruitment platforms have become the dominant channel for modern hiring, yet most platforms offer only basic filtering capabilities, such as job title, keyword, and salary range. This hinders comprehensive analysis of multi-attribute relationships and job market patterns across different scales. We present RecruitScope, a visual analytics system designed to support multidimensional and cross-level exploration of recruitment data for job seekers and employers, particularly HR specialists. Through coordinated visualizations, RecruitScope enables users to analyze job positions and salary patterns from multiple perspectives, interpret industry dynamics at the macro level, and identify emerging positions at the micro level. We demonstrate the effectiveness of RecruitScope through case studies that reveal regional salary distribution patterns, characterize industry growth trajectories, and discover high-demand emerging roles in the job market.

cs.HC↗

arXiv2025

Conch: Competitive Debate Analysis via Visualizing Clash Points and Hierarchical Strategies

In-depth analysis of competitive debates is essential for participants to develop argumentative skills and refine strategies, and further improve their debating performance. However, manual analysis of unstructured and unlabeled textual records of debating is time-consuming and ineffective, as it is challenging to reconstruct contextual semantics and track logical connections from raw data. To address this, we propose Conch, an interactive visualization system that systematically analyzes both what is debated and how it is debated. In particular, we propose a novel parallel spiral visualization that compactly traces the multidimensional evolution of clash points and participant interactions throughout debate process. In addition, we leverage large language models with well-designed prompts to automatically identify critical debate elements such as clash points, disagreements, viewpoints, and strategies, enabling participants to understand the debate context comprehensively. Finally, through two case studies on real-world debates and a carefully-designed user study, we demonstrate Conch's effectiveness and usability for competitive debate analysis.

cs.HC↗

arXiv2025

Machine Learning-Based Prediction of Metal-Organic Framework Materials: A Comparative Analysis of Multiple Models

Metal-organic frameworks (MOFs) have emerged as promising materials for various applications due to their unique structural properties and versatile functionalities. This study presents a comprehensive investigation of machine learning approaches for predicting MOF material properties. We employed five different machine learning models: Random Forest, XGBoost, LightGBM, Support Vector Machine, and Neural Network, to analyze and predict MOF characteristics using a dataset from the Kaggle platform. The models were evaluated using multiple performance metrics, including RMSE, R^2, MAE, and cross-validation scores. Results demonstrated that the Random Forest model achieved superior performance with an R^2 value of 0.891 and RMSE of 0.152, significantly outperforming other models. LightGBM showed remarkable computational efficiency, completing training in 25.7 seconds while maintaining high accuracy. Our comparative analysis revealed that ensemble learning methods generally exhibited better performance than traditional single models in MOF property prediction. This research provides valuable insights into the application of machine learning in materials science and establishes a robust framework for future MOF material design and property prediction.

cs.LG↗