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
Decoupled Recommender Systems: Exploring Alternative Recommender Ecosystem Designs
Anas Buhayh, Elizabeth McKinnie, Robin BurkeUniversity of ColoradoIntrinsic and Extrinsic Factor Disentanglement for Recommendation in Various Context Scenarios
Yixin Su, Wei Jiang, Fangquan Lin, Cheng Yang, Sarah M. Erfani, Junhao Gan, Yunxiang Zhao, Ruixuan Li, Rui ZhangHuazhong University of Science and Technology●Alibaba Group●The University of Melbourne●Beijing Institute of Biotechnology
Multiview graph dual-attention deep learning and contrastive learning for multi-criteria recommender systems
Saman Forouzandeh, Pavel N. Krivitsky, Rohitash ChandraUniversity of New South WalesAgentSociety Challenge: Designing LLM Agents for User Modeling and Recommendation on Web Platforms
Yuwei Yan, Yu Shang, Qingbin Zeng, Yu Li, Keyu Zhao, Zhiheng Zheng, Xuefei Ning, Tianji Wu, Shengen Yan, Yu Wang, Fengli Xu, Yong LiTsinghua University●The Hong Kong University of Science and Technology (Guangzhou)●InfinigenceAI
InstructAgent: Building User Controllable Recommender via LLM Agent
Wujiang Xu, Yunxiao Shi, Zujie Liang, Xuying Ning, Kai Mei, Kun Wang, Xi Zhu, Min Xu, Yongfeng ZhangRutgers University●University of Technology Sydney●Ant Group●University of Illinois●Nanyang Technological UniversityLLM-based User Profile Management for Recommender System
Seunghwan Bang, Hwanjun SongUlsan National Institute of Science and Technology●Korea Advanced Institute of Science and Technology