6 papers
Towards Fair Large Language Model-based Recommender Systems without Costly Retraining
Jin Li, Huilin Gu, Shoujin Wang +5
Large Language Models (LLMs) have revolutionized Recommender Systems (RS) through advanced generative user modeling. However, LLM-based RS (LLM-RS) often inadvertently perpetuates…
Revealing Multimodal Causality with Large Language Models
Jin Li, Shoujin Wang, Qi Zhang +5
Uncovering cause-and-effect mechanisms from data is fundamental to scientific progress. While large language models (LLMs) show promise for enhancing causal discovery (CD) from uns…
A Survey on Progress in LLM Alignment from the Perspective of Reward Design
Miaomiao Ji, Yanqiu Wu, Zhibin Wu +4
Reward design plays a pivotal role in aligning large language models (LLMs) with human values, serving as the bridge between feedback signals and model optimization. This survey pr…
Generating with Fairness: A Modality-Diffused Counterfactual Framework for Incomplete Multimodal Recommendations
Jin Li, Shoujin Wang, Qi Zhang +2
Incomplete scenario is a prevalent, practical, yet challenging setting in Multimodal Recommendations (MMRec), where some item modalities are missing due to various factors. Recentl…
Causal Learning for Trustworthy Recommender Systems: A Survey
Jin Li, Shoujin Wang, Qi Zhang +5
Recommender Systems (RS) have significantly advanced online content filtering and personalized decision-making. However, emerging vulnerabilities in RS have catalyzed a paradigm sh…
NeuroClips: Towards High-fidelity and Smooth fMRI-to-Video Reconstruction
Zixuan Gong, Guangyin Bao, Qi Zhang +9
Reconstruction of static visual stimuli from non-invasion brain activity fMRI achieves great success, owning to advanced deep learning models such as CLIP and Stable Diffusion. How…