Papers by Guohua Wang
Why Multi-Interest Fairness Matters: Hypergraph Contrastive Multi-Interest Learning for Fair Conversational Recommender System (2025.findings-acl)
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| Challenge: | Unfairness is a well-known challenge in Recommender Systems (RSs) some approaches have started to improve fairness in offline or static contexts, but it often exacerbates over time, leading to significant problems like the Matthew effect, filter bubbles, and echo chambers. |
| Approach: | They propose a framework to promote multi-interest diversity fairness in RSs by establishing diverse hypergraphs through contrastive learning. |
| Outcome: | The proposed framework achieves state-of-the-art performance while effectively alleviating unfairness in two CRS-based datasets. |
RTADev: Intention Aligned Multi-Agent Framework for Software Development (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) are efficient assistants to humans in software development tasks, but they can cause errors during the development process. |
| Approach: | They propose an intention aligned multi-agent framework that ensures that all agents work based on a consensus. |
| Outcome: | The proposed framework reduces errors and improves the quality of generated software code. |
CMHKF: Cross-Modality Heterogeneous Knowledge Fusion for Weakly Supervised Video Anomaly Detection (2025.acl-long)
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| Challenge: | Existing methods focus mainly on visual modalities, neglecting rich multi-modality information. |
| Approach: | They propose a framework that integrates cross-modality knowledge from video, audio and text to improve anomaly detection and localization. |
| Outcome: | The proposed framework improves detection and localization of anomalies using video-level labels. |
HyCoRec: Hypergraph-Enhanced Multi-Preference Learning for Alleviating Matthew Effect in Conversational Recommendation (2024.acl-long)
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| Challenge: | Existing methods to study the Matthew effect in Recommender Systems (RSs) however, it is amplified when the user interacts with the system over time. |
| Approach: | They propose a paradigm to alleviate the Matthew effect in conversational recommendation by learning multi-aspect preferences. |
| Outcome: | The proposed paradigm achieves state-of-the-art performance and superior of alleviating Matthew effect in conversational recommendation tasks. |
A Two-phase Prototypical Network Model for Incremental Few-shot Relation Classification (2020.coling-main)
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| Challenge: | Existing supervised and distantly supervised RC models ignore the emergence of novel relations in open environment. |
| Approach: | They propose a two-phase prototypical network with prototype attention alignment and triplet loss to dynamically recognize the novel relations with a few support instances without catastrophic forgetting. |
| Outcome: | Experiments show that the proposed model performs better on deep learning and few-shot learning . it can recognize the novel relations with a few support instances without catastrophic forgetting . |
Knowledge-Guided Cross-Topic Visual Question Generation (2024.lrec-main)
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| Challenge: | Existing methods for visual question generation use answers or question types as constraints to generate questions. |
| Approach: | They propose a knowledge-guided cross-topic visual question generation task to generate unseen topics in cross-section scenarios. |
| Outcome: | The proposed model outperforms baselines and can generate unseen topic-related questions in cross-topic scenarios. |
HyperCRS: Hypergraph-Aware Multi-Grained Preference Learning to Burst Filter Bubbles in Conversational Recommendation System (2025.findings-acl)
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Yongsen Zheng, Mingjie Qian, Guohua Wang, Yang Liu, Ziliang Chen, Mingzhi Mao, Liang Lin, Kwok-Yan Lam
| Challenge: | Existing methods to analyze filter bubbles in the static recommendation environment are unable to burst them during user interactions. |
| Approach: | They propose a paradigm to learn multi-grained user preferences during dynamic user-system interactions via natural language conversations to burst filter bubbles. |
| Outcome: | The proposed paradigm achieves state-of-the-art performance and the superior of bursting filter bubbles in the conversational recommendation system. |
Mitigating Matthew Effect: Multi-Hypergraph Boosted Multi-Interest Self-Supervised Learning for Conversational Recommendation (2024.emnlp-main)
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| Challenge: | Existing methods to mitigate Matthew effect in offline recommendation systems are not effective . a number of studies have identified two root causes for the Matthew effect . |
| Approach: | They propose a framework to address the Matthew effect in conversational recommendation systems . they build hypergraphs to learn multi-level user interests to alleviate the Matthew effec . |
| Outcome: | The proposed framework achieves state-of-the-art performance on four CRS-based datasets . it improves on item-, entity-, word-oriented multiple-channel hypergraphs compared with existing methods . |