Papers by Kwok-Yan Lam
Why Multi-Interest Fairness Matters: Hypergraph Contrastive Multi-Interest Learning for Fair Conversational Recommender System (2025.findings-acl)
Copied to clipboard
| 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. |
Spectra: A Mechanistic Interpretability Library for Vision-Language Models (2026.acl-demo)
Copied to clipboard
| Challenge: | Existing interpretability tools for visionlanguage models are limited to activation probing and saving. |
| Approach: | They propose a library specifically designed for mechanistic interpretability of visionlanguage models that provides unified abstractions for activation patching, attention pattern analysis, and meta-functions across diverse VLM architectures. |
| Outcome: | The proposed library handles architecture-specific complexities while maintaining a simple, high-level interface. |
HyperCRS: Hypergraph-Aware Multi-Grained Preference Learning to Burst Filter Bubbles in Conversational Recommendation System (2025.findings-acl)
Copied to clipboard
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. |
Knowledge Is Flat: A Seq2Seq Generative Framework for Various Knowledge Graph Completion (2022.coling-1)
Copied to clipboard
| Challenge: | Knowledge Graph Completion (KGC) has been extended to multiple knowledge graph (KG) structures, initiating new research directions, e.g. static KGC, temporal KGC and few-shot KGC. |
| Approach: | They propose a generative framework that could tackle different verbalizable graph structures by unifying the representation of KG facts into "flat" text. |
| Outcome: | The proposed framework outperforms many competitive baselines and sets new state-of-the-art performance on five benchmarks. |
Mitigating Matthew Effect: Multi-Hypergraph Boosted Multi-Interest Self-Supervised Learning for Conversational Recommendation (2024.emnlp-main)
Copied to clipboard
| 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 . |
Dipping PLMs Sauce: Bridging Structure and Text for Effective Knowledge Graph Completion via Conditional Soft Prompting (2023.findings-acl)
Copied to clipboard
| Challenge: | Knowledge Graph Completion (KGC) often requires both KG structural and textual information to be effective. |
| Approach: | They propose a system which tunes the parameters of Conditional Soft Prompts generated by entities and relations representations to maintain a balance between textual and structural knowledge. |
| Outcome: | The proposed components outperform baseline models on three static and temporal benchmarks. |