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
Performance Comparison of Session-based Recommendation Algorithms based on GNNs
Faisal Shehzad, Dietmar Jannach
RDGCL: Reaction-Diffusion Graph Contrastive Learning for Recommendation
Jeongwhan Choi, Hyowon Wi, Chaejeong Lee, Sung-Bae Cho, Dongha Lee, Noseong Park
RL-MPCA: A Reinforcement Learning Based Multi-Phase Computation Allocation Approach for Recommender Systems
Jiahong Zhou, Shunhui Mao, Guoliang Yang, Bo Tang, Qianlong Xie, Lebin Lin, Xingxing Wang, Dong Wang