3 papers
cs.IR2026
Accelerating Generative Recommendation via Simple Categorical User Sequence Compression
Qijiong Liu, Lu Fan, Zhongzhou Liu +7
Although generative recommenders demonstrate improved performance with longer sequences, their real-time deployment is hindered by substantial computational costs. To address this…
cs.IR2025
Revisiting scalable sequential recommendation with Multi-Embedding Approach and Mixture-of-Experts
Qiushi Pan, Hao Wang, Guoyuan An +3
In recommendation systems, how to effectively scale up recommendation models has been an essential research topic. While significant progress has been made in developing advanced a…
cs.CV2025
Large Language Models Facilitate Vision Reflection in Image Classification
Guoyuan An, JaeYoon Kim, SungEui Yoon
This paper presents several novel findings on the explainability of vision reflection in large multimodal models (LMMs). First, we show that prompting an LMM to verify the predicti…