4 citations · 6 across the 9 of their papers we have counts for
12 papers
OSR: Output Space Redistribution for Adaptive Label Removal in Classification Models
Minyi Peng, Darian Gunamardi, Ivan Tjuawinata +2
Label removal occurs frequently in classification systems with evolving taxonomies, where categories must be dynamically updated or eliminated. To accommodate such changes, classif…
FacetCRS: Multi-Faceted Preference Learning for Pricking Filter Bubbles in Conversational Recommender System
Yongsen Zheng, Ziliang Chen, Jinghui Qin +1
The filter bubble is a notorious issue in Recommender Systems (RSs), which describes the phenomenon whereby users are exposed to a limited and narrow range of information or conten…
Mitigating Matthew Effect: Multi-Hypergraph Boosted Multi-Interest Self-Supervised Learning for Conversational Recommendation
Yongsen Zheng, Ruilin Xu, Guohua Wang +2
The Matthew effect is a big challenge in Recommender Systems (RSs), where popular items tend to receive increasing attention, while less popular ones are often overlooked, perpetua…
HyCoRec: Hypergraph-Enhanced Multi-Preference Learning for Alleviating Matthew Effect in Conversational Recommendation
Yongsen Zheng, Ruilin Xu, Ziliang Chen +4
The Matthew effect is a notorious issue in Recommender Systems (RSs), \emph{i.e.}, the rich get richer and the poor get poorer, wherein popular items are overexposed while less pop…
Lethe: Purifying Backdoored Large Language Models with Knowledge Dilution
Chen Chen, Yuchen Sun, Jiaxin Gao +5
Large language models (LLMs) have seen significant advancements, achieving superior performance in various Natural Language Processing (NLP) tasks. However, they remain vulnerable…
Why Multi-Interest Fairness Matters: Hypergraph Contrastive Multi-Interest Learning for Fair Conversational Recommender System
Yongsen Zheng, Zongxuan Xie, Guohua Wang +3
Unfairness is a well-known challenge in Recommender Systems (RSs), often resulting in biased outcomes that disadvantage users or items based on attributes such as gender, race, age…