25 citations · 43 across the 6 of their papers we have counts for
6 papers
Generative Large Recommendation Models: Emerging Trends in LLMs for Recommendation
Hao Wang, Wei Guo, Luankang Zhang +7
In the era of information overload, recommendation systems play a pivotal role in filtering data and delivering personalized content. Recent advancements in feature interaction and…
The 2nd Workshop on Recommendation with Generative Models
Wenjie Wang, Yang Zhang, Xinyu Lin +7
The rise of generative models has driven significant advancements in recommender systems, leaving unique opportunities for enhancing users' personalized recommendations. This works…
APGL4SR: A Generic Framework with Adaptive and Personalized Global Collaborative Information in Sequential Recommendation
Mingjia Yin, Hao Wang, Xiang Xu +7
The sequential recommendation system has been widely studied for its promising effectiveness in capturing dynamic preferences buried in users' sequential behaviors. Despite the con…
Unsupervised Representation Learning for Time Series: A Review
Qianwen Meng, Hangwei Qian, Yong Liu +3
Unsupervised representation learning approaches aim to learn discriminative feature representations from unlabeled data, without the requirement of annotating every sample. Enablin…
Layer-refined Graph Convolutional Networks for Recommendation
Xin Zhou, Donghui Lin, Yong Liu +1
Recommendation models utilizing Graph Convolutional Networks (GCNs) have achieved state-of-the-art performance, as they can integrate both the node information and the topological…
Minimalist and High-performance Conversational Recommendation with Uncertainty Estimation for User Preference
Yinan Zhang, Boyang Li, Yong Liu +2
Conversational recommendation system (CRS) is emerging as a user-friendly way to capture users' dynamic preferences over candidate items and attributes. Multi-shot CRS is designed…