43 citations · 181 across the 27 of their papers we have counts for
6 papers · 1 filter
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…
Robust Training Objectives Improve Embedding-based Retrieval in Industrial Recommendation Systems
Matthew Kolodner, Mingxuan Ju, Zihao Fan +5
Improving recommendation systems (RS) can greatly enhance the user experience across many domains, such as social media. Many RS utilize embedding-based retrieval (EBR) approaches…
How Does Message Passing Improve Collaborative Filtering?
Mingxuan Ju, William Shiao, Zhichun Guo +4
Collaborative filtering (CF) has exhibited prominent results for recommender systems and been broadly utilized for real-world applications. A branch of research enhances CF methods…
Improving Out-of-Vocabulary Handling in Recommendation Systems
William Shiao, Mingxuan Ju, Zhichun Guo +5
Recommendation systems (RS) are an increasingly relevant area for both academic and industry researchers, given their widespread impact on the daily online experiences of billions…
USE: Dynamic User Modeling with Stateful Sequence Models
Zhihan Zhou, Qixiang Fang, Leonardo Neves +5
User embeddings play a crucial role in user engagement forecasting and personalized services. Recent advances in sequence modeling have sparked interest in learning user embeddings…
Node Duplication Improves Cold-start Link Prediction
Zhichun Guo, Tong Zhao, Yozen Liu +5
Graph Neural Networks (GNNs) are prominent in graph machine learning and have shown state-of-the-art performance in Link Prediction (LP) tasks. Nonetheless, recent studies show tha…