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
TRAWL: External Knowledge-Enhanced Recommendation with LLM Assistance
Weiqing Luo, Chonggang Song, Lingling Yi, Gong Cheng
RecAI: Leveraging Large Language Models for Next-Generation Recommender Systems
Jianxun Lian, Yuxuan Lei, Xu Huang, Jing Yao, Wei Xu, Xing Xie
Pre-Trained Model Recommendation for Downstream Fine-tuning
Jiameng Bai, Sai Wu, Jie Song, Junbo Zhao, Gang Chen
LiMAML: Personalization of Deep Recommender Models via Meta Learning
Ruofan Wang, Prakruthi Prabhakar, Gaurav Srivastava, Tianqi Wang, Zeinab S. Jalali, Varun Bharill, Yunbo Ouyang, Aastha Nigam, Divya Venugopalan, Aman Gupta, Fedor Borisyuk, Sathiya Keerthi, Ajith Muralidharan
EasyRL4Rec: An Easy-to-use Library for Reinforcement Learning Based Recommender Systems
Yuanqing Yu, Chongming Gao, Jiawei Chen, Heng Tang, Yuefeng Sun, Qian Chen, Weizhi Ma, Min Zhang