3 papers
cs.LG2026
How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization
Xinyi Wu, Siyuan Liu, Ali Jadbabaie
Rotary Position Embeddings (RoPE) provide transformers with a fixed grid of positional frequencies, yet trained models use these frequencies highly non-uniformly. We study what det…
cs.IR2025
Understanding and Scaling Collaborative Filtering Optimization from the Perspective of Matrix Rank
Donald Loveland, Xinyi Wu, Tong Zhao +3
Collaborative Filtering (CF) methods dominate real-world recommender systems given their ability to learn high-quality, sparse ID-embedding tables that effectively capture user pre…
cs.IR2025
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…