1 citations · 1 across the 6 of their papers we have counts for
9 papers
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…
JoyAI-Sim: A Simulation-Enabled Interconversion Toolchain for the Embodied Data Pyramid
Peidong Liu, Yongce Liu, Songyan Guo +34
Generalist robot policies require trustworthy evaluation and robot-usable training data, but both are difficult to scale with physical robots alone. Real-robot trials and demonstra…
HoloRec: Holistic Encoding and Interleaved Reasoning for Generative Recommendation
Shuqi Zhao, Jingsong Su, Xiang Liu +9
Generative recommendation models that formulate the task as sequence generation overcome the objective fragmentation problem of traditional cascade architectures, yet existing appr…
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…
ODMixer: Fine-grained Spatial-temporal MLP for Metro Origin-Destination Prediction
Yang Liu, Binglin Chen, Yongsen Zheng +3
Metro Origin-Destination (OD) prediction is a crucial yet challenging spatial-temporal prediction task in urban computing, which aims to accurately forecast cross-station ridership…
Diagnosing and Rectifying Fake OOD Invariance: A Restructured Causal Approach
Ziliang Chen, Yongsen Zheng, Zhao-Rong Lai +2
Invariant representation learning (IRL) encourages the prediction from invariant causal features to labels de-confounded from the environments, advancing the technical roadmap of o…
FRAME Revisited: An Interpretation View Based on Particle Evolution
Xu Cai, Yang Wu, Guanbin Li +2
FRAME (Filters, Random fields, And Maximum Entropy) is an energy-based descriptive model that synthesizes visual realism by capturing mutual patterns from structural input signals.…