5 citations · 5 across the 5 of their papers we have counts for
7 papers
Have We Really Understood Collaborative Information? An Empirical Investigation
Xiaokun Zhang, Zhaochun Ren, Bowei He +2
Collaborative information serves as the cornerstone of recommender systems which typically focus on capturing it from user-item interactions to deliver personalized services. Howev…
Beyond Higher Rank: Token-wise Input-Output Projections for Efficient Low-Rank Adaptation
Shiwei Li, Xiandi Luo, Haozhao Wang +6
Low-rank adaptation (LoRA) is a parameter-efficient fine-tuning (PEFT) method widely used in large language models (LLMs). LoRA essentially describes the projection of an input spa…
Who Stole Your Data? A Method for Detecting Unauthorized RAG Theft
Peiyang Liu, Ziqiang Cui, Di Liang +1
Retrieval-augmented generation (RAG) enhances Large Language Models (LLMs) by mitigating hallucinations and outdated information issues, yet simultaneously facilitates unauthorized…
Queries Are Not Alone: Clustering Text Embeddings for Video Search
Peyang Liu, Xi Wang, Ziqiang Cui +1
The rapid proliferation of video content across various platforms has highlighted the urgent need for advanced video retrieval systems. Traditional methods, which primarily depend…
BoRA: Towards More Expressive Low-Rank Adaptation with Block Diversity
Shiwei Li, Xiandi Luo, Haozhao Wang +6
Low-rank adaptation (LoRA) is a parameter-efficient fine-tuning (PEFT) method widely used in large language models (LLMs). It approximates the update of a pretrained weight matrix…
Shapley Value-driven Data Pruning for Recommender Systems
Yansen Zhang, Xiaokun Zhang, Ziqiang Cui +1
Recommender systems often suffer from noisy interactions like accidental clicks or popularity bias. Existing denoising methods typically identify users' intent in their interaction…