Recommender System
Recommender systems aim to predict user preferences and provide personalized recommendations, enhancing user experience across various online platforms. Current research emphasizes improving accuracy and mitigating biases, focusing on advanced techniques like neural networks (including transformers and recurrent networks), matrix factorization, and ensemble methods to address challenges such as data sparsity, outlier detection, and the impact of algorithmic bias on user preferences. This field is significant due to its widespread applications and the growing need for responsible and ethical design, driving research into explainability, fairness, and the use of causal inference to understand and mitigate the societal impact of these systems.
Papers
Conf-GNNRec: Quantifying and Calibrating the Prediction Confidence for GNN-based Recommendation Methods
Meng Yan, Cai Xu, Xujing Wang, Ziyu Guan, Wei Zhao, Yuhang ZhouXidian University●Communication University Of ChinaBeyond Static Testbeds: An Interaction-Centric Agent Simulation Platform for Dynamic Recommender Systems
Song Jin, Juntian Zhang, Yuhan Liu, Xun Zhang, Yufei Zhang, Guojun Yin, Fei Jiang, Wei Lin, Rui YanRenmin University of China●Meituan●Wuhan University
Field Matters: A lightweight LLM-enhanced Method for CTR Prediction
Yu Cui, Feng Liu, Jiawei Chen, Xingyu Lou, Changwang Zhang, Jun Wang, Yuegang Sun, Xiaohu Yang, Can WangZhejiang University●OPPO Research Institute●Intelligence IndeedTranSUN: A Preemptive Paradigm to Eradicate Retransformation Bias Intrinsically from Regression Models in Recommender Systems
Jiahao Yu, Haozhuang Liu, Yeqiu Yang, Lu Chen, Wu Jian, Yuning Jiang, Bo ZhengAlibaba Group
The Pitfalls of Growing Group Complexity: LLMs and Social Choice-Based Aggregation for Group Recommendations
Cedric Waterschoot, Nava Tintarev, Francesco BarileMaastricht UniversityPrompt-Based LLMs for Position Bias-Aware Reranking in Personalized Recommendations
Md Aminul Islam, Ahmed Sayeed FarukUniversity of Illinois Chicago
SimUSER: Simulating User Behavior with Large Language Models for Recommender System Evaluation
Nicolas Bougie, Narimasa WatanabeWoven by ToyotaCollaborative Learning of On-Device Small Model and Cloud-Based Large Model: Advances and Future Directions
Chaoyue Niu, Yucheng Ding, Junhui Lu, Zhengxiang Huang, Hang Zeng, Yutong Dai, Xuezhen Tu, Chengfei Lv, Fan Wu, Guihai Chen