Kunpeng Liu

Multi-level Recommendation Reasoning over Knowledge Graphs with Reinforcement Learning

2022 · Proceedings of the ACM Web Conference 2022

Knowledge graphs (KGs) have been widely used to improve recommendation accuracy. The multi-hop paths on KGs also enable recommendation reasoning, which is considered a crystal type of explainability. In this paper, we propose a reinforcement …

Dynamic and Adaptive Feature Generation with LLM

2024 · arXiv (Cornell University)

The representation of feature space is a crucial environment where data points get vectorized and embedded for subsequent modeling. Thus the efficacy of machine learning (ML) algorithms is closely related to the quality of feature …

Dual-Agent Reinforcement Learning for Automated Feature Generation

2024

Feature generation involves creating new features from raw data to capture complex relationships among the original features, improving model robustness and machine learning performance. Current methods using reinforcement learning for feature generation have made feature …

Two-Stage Feature Generation with Transformer and Reinforcement Learning

2025 · arXiv (Cornell University)

Feature generation is a critical step in machine learning, aiming to enhance model performance by capturing complex relationships within the data and generating meaningful new features. Traditional feature generation methods heavily rely on domain expertise …

Deep Cut-informed Graph Embedding and Clustering

2025 · arXiv (Cornell University)

Graph clustering aims to divide the graph into different clusters. The recently emerging deep graph clustering approaches are largely built on graph neural networks (GNN). However, GNN is designed for general graph encoding and there …