Recommender Systems

PSL: Rethinking and Improving Softmax Loss from Pairwise Perspective for Recommendation

Softmax Loss (SL) is widely applied in recommender systems (RS) and has demonstrated effectiveness. This work analyzes SL from a …

Weiqin Yang, Jiawei Chen, Yan Feng, Chun Chen, Can Wang

CDR: Conservative Doubly Robust Learning for Debiased Recommendation

In recommendation systems (RS), user behavior data is observational rather than experimental, resulting in widespread bias in the data. …

ZijieSong, Jiawei Chen, ShengZhou, Qihao Shi, Yan Feng, Chun Chen, Can Wang

How Do Recommendation Models Amplify Popularity Bias? An Analysis from the Spectral Perspective

Reinforcement Learning (RL)-Based Recommender Systems (RSs) have gained rising attention for their potential to enhance long-term user …

YuanqingYu, ChongmingGao, Jiawei Chen, Heng Tang, YuefengSun, QianChen, WeizhiMa, MinZhang

ReLand: Integrating Large Language Models' Insights into Industrial Recommenders via a Controllable Reasoning Pool

Recently, Large Language Models (LLMs) have shown significant potential in addressing the isolation issues faced by recommender …

ChangxinTian, BinbinHu, ChunjingGan, HaoyuChen, ZhuoZhang, LiYu, ZiqiLiu, ZhiqiangZhang, JunZhou, Jiawei Chen