4 papers · 1 filter
InfoDCL: Informative Noise Enhanced Diffusion Based Contrastive Learning
Xufeng Liang, Zhida Qin, Chong Zhang +2
Contrastive learning has demonstrated promising potential in recommender systems. Existing methods typically construct sparser views by randomly perturbing the original interaction…
Time Matters: Enhancing Sequential Recommendations with Time-Guided Graph Neural ODEs
Haoyan Fu, Zhida Qin, Shixiao Yang +5
Sequential recommendation (SR) is widely deployed in e-commerce platforms, streaming services, etc., revealing significant potential to enhance user experience. However, existing m…
From Local Details to Global Context: Advancing Vision-Language Models with Attention-Based Selection
Lincan Cai, Jingxuan Kang, Shuang Li +4
Pretrained vision-language models (VLMs), e.g., CLIP, demonstrate impressive zero-shot capabilities on downstream tasks. Prior research highlights the crucial role of visual augmen…
Large Language Models Enhanced Hyperbolic Space Recommender Systems
Wentao Cheng, Zhida Qin, Zexue Wu +2
Large Language Models (LLMs) have attracted significant attention in recommender systems for their excellent world knowledge capabilities. However, existing methods that rely on Eu…