2 citations · 2 across the 4 of their papers we have counts for
4 papers
Enhancing Item Tokenization for Generative Recommendation through Self-Improvement
Runjin Chen, Mingxuan Ju, Ngoc Bui +7
Generative recommendation systems, driven by large language models (LLMs), present an innovative approach to predicting user preferences by modeling items as token sequences and ge…
GraphHash: Graph Clustering Enables Parameter Efficiency in Recommender Systems
Xinyi Wu, Donald Loveland, Runjin Chen +7
Deep recommender systems rely heavily on large embedding tables to handle high-cardinality categorical features such as user/item identifiers, and face significant memory constrain…
Found in the Middle: How Language Models Use Long Contexts Better via Plug-and-Play Positional Encoding
Zhenyu Zhang, Runjin Chen, Shiwei Liu +5
This paper aims to overcome the "lost-in-the-middle" challenge of large language models (LLMs). While recent advancements have successfully enabled LLMs to perform stable language…
LLaGA: Large Language and Graph Assistant
Runjin Chen, Tong Zhao, Ajay Jaiswal +2
Graph Neural Networks (GNNs) have empowered the advance in graph-structured data analysis. Recently, the rise of Large Language Models (LLMs) like GPT-4 has heralded a new era in d…