5 papers
TIDE-MC: Two-Sided Interpolative Decomposition for Billion-Scale GPU Matrix Completion
Chengying Huan, Yubo Wang, Pinhuan Wang +11
Matrix completion supports large-scale recommendation and scientific computing, yet existing GPU solvers commonly assume that the observed matrix or its dense factors fit in device…
OrchANN: Hierarchical Orchestration for Skewed Out-of-Core Vector Search
Lizheng Chen, Pinhuan Wang, Shaonan Ma +9
At billion scale, approximate nearest neighbor search (ANNS) often becomes an out-of-core problem: the full vector collection and index structures exceed memory capacity, making qu…
Token-Controlled Re-ranking for Sequential Recommendation via LLMs
Wenxi Dai, Wujiang Xu, Pinhuan Wang +1
The widespread adoption of Large Language Models (LLMs) as re-rankers is shifting recommender systems towards a user-centric paradigm. However, a significant gap remains: current r…
REALM: Recursive Relevance Modeling for LLM-based Document Re-Ranking
Pinhuan Wang, Zhiqiu Xia, Chunhua Liao +2
Large Language Models (LLMs) have shown strong capabilities in document re-ranking, a key component in modern Information Retrieval (IR) systems. However, existing LLM-based approa…
Bingo: Radix-based Bias Factorization for Random Walk on Dynamic Graphs
Pinhuan Wang, Chengying Huan, Zhibin Wang +3
Random walks are a primary means for extracting information from large-scale graphs. While most real-world graphs are inherently dynamic, state-of-the-art random walk engines faile…