8 papers
Beyond Fixed Depths and Widths: Optimizing Textual Decoding Tries in LLM-based Generative Recommendation
Jingzhe Liu, Hanbing Wang, Jiliang Tang +4
Generative recommendation (GR) is an increasingly popular paradigm in recommender systems, with a prominent line of work using LLMs as autoregressive backbones to predict the next…
Understanding Generative Recommendation with Semantic IDs from a Model-scaling View
Jingzhe Liu, Liam Collins, Jiliang Tang +3
Recent advancements in generative models have allowed the emergence of a promising paradigm for recommender systems (RS), known as Generative Recommendation (GR), which tries to un…
GSTBench: A Benchmark Study on the Transferability of Graph Self-Supervised Learning
Yu Song, Zhigang Hua, Yan Xie +3
Self-supervised learning (SSL) has shown great promise in graph representation learning. However, most existing graph SSL methods are developed and evaluated under a single-dataset…
A Scalable Pretraining Framework for Link Prediction with Efficient Adaptation
Yu Song, Zhigang Hua, Harry Shomer +4
Link Prediction (LP) is a critical task in graph machine learning. While Graph Neural Networks (GNNs) have significantly advanced LP performance recently, existing methods face key…
One Model for One Graph: A New Perspective for Pretraining with Cross-domain Graphs
Jingzhe Liu, Haitao Mao, Zhikai Chen +6
Graph Neural Networks (GNNs) have emerged as a powerful tool to capture intricate network patterns, achieving success across different domains. However, existing GNNs require caref…
Higher-order Structure Boosts Link Prediction on Temporal Graphs
Jingzhe Liu, Zhigang Hua, Yan Xie +5
Temporal Graph Neural Networks (TGNNs) have gained growing attention for modeling and predicting structures in temporal graphs. However, existing TGNNs primarily focus on pairwise…