14 citations · 22 across the 11 of their papers we have counts for
11 papers
Unleash LLMs Potential for Recommendation by Coordinating Twin-Tower Dynamic Semantic Token Generator
Jun Yin, Zhengxin Zeng, Mingzheng Li +11
Owing to the unprecedented capability in semantic understanding and logical reasoning, the pre-trained large language models (LLMs) have shown fantastic potential in developing the…
GPT4Rec: Graph Prompt Tuning for Streaming Recommendation
Peiyan Zhang, Yuchen Yan, Xi Zhang +5
In the realm of personalized recommender systems, the challenge of adapting to evolving user preferences and the continuous influx of new users and items is paramount. Conventional…
PeFAD: A Parameter-Efficient Federated Framework for Time Series Anomaly Detection
Ronghui Xu, Hao Miao, Senzhang Wang +2
With the proliferation of mobile sensing techniques, huge amounts of time series data are generated and accumulated in various domains, fueling plenty of real-world applications. I…
Deep Multi-View Channel-Wise Spatio-Temporal Network for Traffic Flow Prediction
Hao Miao, Senzhang Wang, Meiyue Zhang +3
Accurately forecasting traffic flows is critically important to many real applications including public safety and intelligent transportation systems. The challenges of this proble…
High-Frequency-aware Hierarchical Contrastive Selective Coding for Representation Learning on Text-attributed Graphs
Peiyan Zhang, Chaozhuo Li, Liying Kang +4
We investigate node representation learning on text-attributed graphs (TAGs), where nodes are associated with text information. Although recent studies on graph neural networks (GN…
Macro Graph Neural Networks for Online Billion-Scale Recommender Systems
Hao Chen, Yuanchen Bei, Qijie Shen +6
Predicting Click-Through Rate (CTR) in billion-scale recommender systems poses a long-standing challenge for Graph Neural Networks (GNNs) due to the overwhelming computational comp…