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
Towards Robust Recommender Systems via Triple Cooperative Defense
Qingyang Wang, Defu Lian, Chenwang Wu, Enhong Chen
Recommendation with User Active Disclosing Willingness
Lei Wang, Xu Chen, Quanyu Dai, Zhenhua Dong
FedGRec: Federated Graph Recommender System with Lazy Update of Latent Embeddings
Junyi Li, Heng Huang
Shadfa 0.1: The Iranian Movie Knowledge Graph and Graph-Embedding-Based Recommender System
Rayhane Pouyan, Hadi Kalamati, Hannane Ebrahimian, Mohammad Karrabi, Mohammad-R. Akbarzadeh-T
Simpson's Paradox in Recommender Fairness: Reconciling differences between per-user and aggregated evaluations
Flavien Prost, Ben Packer, Jilin Chen, Li Wei, Pierre Kremp, Nicholas Blumm, Susan Wang, Tulsee Doshi, Tonia Osadebe, Lukasz Heldt, Ed H. Chi, Alex Beutel
Restricted Bernoulli Matrix Factorization: Balancing the trade-off between prediction accuracy and coverage in classification based collaborative filtering
Ángel González-Prieto, Abraham Gutiérrez, Fernando Ortega, Raúl Lara-Cabrera
DreamShard: Generalizable Embedding Table Placement for Recommender Systems
Daochen Zha, Louis Feng, Qiaoyu Tan, Zirui Liu, Kwei-Herng Lai, Bhargav Bhushanam, Yuandong Tian, Arun Kejariwal, Xia Hu