112 citations · 169 across the 6 of their papers we have counts for
8 papers
Explanations from Large Language Models Make Small Reasoners Better
Shiyang Li, Jianshu Chen, Yelong Shen +9
Integrating free-text explanations to in-context learning of large language models (LLM) is shown to elicit strong reasoning capabilities along with reasonable explanations. In thi…
DyGCN: Dynamic Graph Embedding with Graph Convolutional Network
Zeyu Cui, Zekun Li, Shu Wu +4
Graph embedding, aiming to learn low-dimensional representations (aka. embeddings) of nodes, has received significant attention recently. Recent years have witnessed a surge of eff…
Cold-start Sequential Recommendation via Meta Learner
Yujia Zheng, Siyi Liu, Zekun Li +1
This paper explores meta-learning in sequential recommendation to alleviate the item cold-start problem. Sequential recommendation aims to capture user's dynamic preferences based…
Heterogeneous Graph Collaborative Filtering
Zekun Li, Yujia Zheng, Shu Wu +2
Graph-based collaborative filtering (CF) algorithms have gained increasing attention. Existing work in this literature usually models the user-item interactions as a bipartite grap…
DGTN: Dual-channel Graph Transition Network for Session-based Recommendation
Yujia Zheng, Siyi Liu, Zekun Li +1
The task of session-based recommendation is to predict user actions based on anonymous sessions. Recent research mainly models the target session as a sequence or a graph to captur…
Fi-GNN: Modeling Feature Interactions via Graph Neural Networks for CTR Prediction
Zekun Li, Zeyu Cui, Shu Wu +2
Click-through rate (CTR) prediction is an essential task in web applications such as online advertising and recommender systems, whose features are usually in multi-field form. The…