5 papers
LBR: Towards Mitigating Length Bias in Large Language Models for Recommendation
Hongchen Li, Bohao Wang, Jingbang Chen +5
Large language models (LLMs) have recently emerged as powerful backbones for recommender systems by reformulating recommendation as a token-level generation task. Despite their pro…
Scalable Algorithm for Dynamic Quasi-clique Detection
Jingbang Chen, Weinuo Li, Yingli Zhou +4
Identifying dense subgraphs known as quasi-cliques is pivotal in numerous graph mining tasks across domains such as social networks, biology, and e-commerce. While prior work has d…
BEAR: Towards Beam-Search-Aware Optimization for Recommendation with Large Language Models
Weiqin Yang, Bohao Wang, Zhenxiang Xu +5
Recent years have seen a rapid surge in research leveraging Large Language Models (LLMs) for recommendation. These methods typically employ supervised fine-tuning (SFT) to adapt LL…
Finding the Balance Rate of Uncertain Signed Graphs
Zeyu Wang, Kudria Sergei, Jingbang Chen +4
Signed graphs are widely used to analyze complex systems such as social, political, and biological networks. The notion of balance, a key concept of signed graphs, reflects the sta…
TopKGAT: A Top-K Objective-Driven Architecture for Recommendation
Sirui Chen, Jiawei Chen, Canghong Jin +4
Recommendation systems (RS) aim to retrieve the top-K items most relevant to users, with metrics such as Precision@K and Recall@K commonly used to assess effectiveness. The archite…