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Wenchuan Yang

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3 papers

2 author rows

EAAI Journal 2025 Journal Article

Automatic requirements elicitation from user-generated content: A review of data, methods, and representations

Mengsi Cai
Wenchuan Yang
Yonghao Du
Yuejin Tan
Xin Lu

Requirements elicitation (RE) is the first and most important phase in requirements engineering. In the era of big data, as online platforms have become one of the primary channels for users to provide product feedback, extensive research has been conducted to explore how customer requirements can be elicited from user-generated content (UGC) through artificial intelligence technologies such as text clustering, topic modeling, and machine learning. We systematically review the existing literature in the area of UGC-based RE by categorizing research according to three dimensions: UGC data sources for RE, RE methods, and requirement representations. Furthermore, we propose a comprehensive research framework for UGC-based RE and identify key directions for future research. The findings of this study can contribute to the development of more effective and intelligent RE processes, while helping to establish a systematic framework for UGC-based RE research.

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ICML Conference 2025 Conference Paper

ProDiff: Prototype-Guided Diffusion for Minimal Information Trajectory Imputation

Tianci Bu
Le Zhou
Wenchuan Yang
Jianhong Mou
Kang Yang
Suoyi Tan
Feng Yao
Jingyuan Wang

Trajectory data is crucial for various applications but often suffers from incompleteness due to device limitations and diverse collection scenarios. Existing imputation methods rely on sparse trajectory or travel information, such as velocity, to infer missing points. However, these approaches assume that sparse trajectories retain essential behavioral patterns, which place significant demands on data acquisition and overlook the potential of large-scale human trajectory embeddings. To address this, we propose ProDiff, a trajectory imputation framework that uses only two endpoints as minimal information. It integrates prototype learning to embed human movement patterns and a denoising diffusion probabilistic model for robust spatiotemporal reconstruction. Joint training with a tailored loss function ensures effective imputation. ProDiff outperforms state-of-the-art methods, improving accuracy by 6. 28% on FourSquare and 2. 52% on WuXi. Further analysis shows a 0. 927 correlation between generated and real trajectories, demonstrating the effectiveness of our approach.

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EAAI Journal 2023 Journal Article

An improved heterogeneous graph convolutional network for job recommendation

Hao Wang
Wenchuan Yang
Jichao Li
Junwei Ou
Yanjie Song
Yingwu Chen

Job recommendation is crucial in online recruitment platforms due to the overwhelming number of job postings. Job seekers spend considerable time and effort searching for suitable employment. With millions of job seekers browsing job postings daily, the demand for accurate and effective job recommendations is more pressing than ever. To address this challenge, we propose IHGCN, an improved semi-supervised heterogeneous graph convolutional network model for job recommendation. IHGCN aims to provide job recommendations for early job seekers based on their resumes. Firstly, we introduce a novel labeling classification standard specifically tailored to early job seeker resumes. Secondly, we construct a heterogeneous resume graph where each resume is represented as a node. Job recommendation is treated as a multi-classification problem. Thirdly, our IHGCN model learns a node representation from the graph to perform effective job recommendations. To evaluate our model, we conduct experiments using a real-world resume dataset obtained from LinkedIn. The results demonstrate that IHGCN outperforms the baselines by around 10%. This study highlights the benefits of leveraging meta-paths within the Graph Convolutional Network model to address the sparsity problem caused by the one-hot representation of nodes.

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