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
Leveraging LLM Reasoning Enhances Personalized Recommender Systems
Alicia Y. Tsai, Adam Kraft, Long Jin, Chenwei Cai, Anahita Hosseini, Taibai Xu, Zemin Zhang, Lichan Hong, Ed H. Chi, Xinyang Yi
Dual Test-time Training for Out-of-distribution Recommender System
Xihong Yang, Yiqi Wang, Jin Chen, Wenqi Fan, Xiangyu Zhao, En Zhu, Xinwang Liu, Defu Lian
A Comprehensive Review of Recommender Systems: Transitioning from Theory to Practice
Shaina Raza, Mizanur Rahman, Safiullah Kamawal, Armin Toroghi, Ananya Raval, Farshad Navah, Amirmohammad Kazemeini
ROLeR: Effective Reward Shaping in Offline Reinforcement Learning for Recommender Systems
Yi Zhang, Ruihong Qiu, Jiajun Liu, Sen Wang
On Causally Disentangled State Representation Learning for Reinforcement Learning based Recommender Systems
Siyu Wang, Xiaocong Chen, Lina Yao
Performative Debias with Fair-exposure Optimization Driven by Strategic Agents in Recommender Systems
Zhichen Xiang, Hongke Zhao, Chuang Zhao, Ming He, Jianping Fan
A Thorough Performance Benchmarking on Lightweight Embedding-based Recommender Systems
Hung Vinh Tran, Tong Chen, Quoc Viet Hung Nguyen, Zi Huang, Lizhen Cui, Hongzhi Yin