19 citations · 20 across the 73 of their papers we have counts for
6 papers · 1 filter
SAHC-NS: Structure-Aware and Hardness-Calibrated Negative Sampling for Implicit Collaborative Filtering
Jiayi Wu, Zhengyu Wu, Xunkai Li +3
Negative sampling is a key component of implicit collaborative filtering (CF), as it enables recommenders to effectively learn user preferences. Existing negative sampling methods…
MAGE-RAG: Multigranular Adaptive Graph Evidence for Agentic Multimodal RAG in Long-Document QA
Yilong Zuo, Xunkai Li, Jing Yuan +3
Long-document multimodal question answering requires a system to locate sparse evidence in long PDFs and integrate clues from text, tables, images, charts, and complex layouts. Exi…
TFPS: A Temporal Filtration-enhanced Positive Sample Set Construction Method for Implicit Collaborative Filtering
Jiayi Wu, Zhengyu Wu, Xunkai Li +2
The negative sampling strategy can effectively train collaborative filtering (CF) recommendation models based on implicit feedback by constructing positive and negative samples. Ho…
A Topology-Aware Positive Sample Set Construction and Feature Optimization Method in Implicit Collaborative Filtering
Jiayi Wu, Zhengyu Wu, Xunkai Li +2
Negative sampling strategies are widely used in implicit collaborative filtering to address issues like data sparsity and class imbalance. However, these methods often introduce fa…
A Simple yet Effective Negative Sampling Plugin for Constructing Positive Sample Pairs in Implicit Collaborative Filtering
Jiayi Wu, Zhengyu Wu, Xunkai Li +2
Most implicit collaborative filtering (CF) models are trained with negative sampling, where existing work designs sophisticated strategies for high-quality negatives while largely…
Dual-Tree LLM-Enhanced Negative Sampling for Implicit Collaborative Filtering
Jiayi Wu, Zhengyu Wu, Xunkai Li +2
Negative sampling is a pivotal technique in implicit collaborative filtering (CF) recommendation, enabling efficient and effective training by contrasting observed interactions wit…