7 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…
The Pitfall of Scaling Up: Uncovering and Mitigating Popularity Bias Amplification in Scaling Transformer-based Recommenders
Weiqin Yang, Yue Pan, Chongming Gao +4
We identify a critical pitfall in scaling transformer-based sequential recommenders: while increasing model size improves recommendation accuracy, it simultaneously amplifies popul…
Trustworthy Recommendation in the Era of Large Language Models: Opportunities and Challenges
Bohao Wang, Yu Cui, Zhenxiang Xu +13
The field of recommender systems (RS) is currently undergoing two profound paradigm shifts. From the perspective of objectives, the goal has shifted beyond mere recommendation accu…
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…
Beyond Static Best-of-N: Bayesian List-wise Alignment for LLM-based Recommendation
Ruijun Chen, Chongming Gao, Jiawei Chen +2
Large Language Models have revolutionized recommender systems (LLM4Rec) by leveraging their generative capabilities to model complex user preferences. However, existing LLM4Rec met…
Talos: Optimizing Top- Accuracy in Recommender Systems
Shengjia Zhang, Weiqin Yang, Jiawei Chen +5
Recommender systems (RS) aim to retrieve a small set of items that best match individual user preferences. Naturally, RS place primary emphasis on the quality of the Top- result…