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
Embedding Cultural Diversity in Prototype-based Recommender Systems
Armin Moradi, Nicola Neophytou, Florian Carichon, Golnoosh Farnadi
Large Language Model Enhanced Recommender Systems: Taxonomy, Trend, Application and Future
Qidong Liu, Xiangyu Zhao, Yuhao Wang, Yejing Wang, Zijian Zhang, Yuqi Sun, Xiang Li, Maolin Wang, Pengyue Jia, Chong Chen, Wei Huang, Feng Tian
A multi-theoretical kernel-based approach to social network-based recommendation
Xin Li, Mengyue Wang, T.-P. Liang
Modeling the Heterogeneous Duration of User Interest in Time-Dependent Recommendation: A Hidden Semi-Markov Approach
Haidong Zhang, Wancheng Ni, Xin Li, Yiping Yang
RecSys Arena: Pair-wise Recommender System Evaluation with Large Language Models
Zhuo Wu, Qinglin Jia, Chuhan Wu, Zhaocheng Du, Shuai Wang, Zan Wang, Zhenhua Dong