Robust Representation Learning for Unified Online Top-K Recommendation

no code implementations • 24 Oct 2023 • Minfang Lu, Yuchen Jiang, Huihui Dong, Qi Li, Ziru Xu, Yuanlin Liu, Lixia Wu, Haoyuan Hu, Han Zhu, Yuning Jiang, Jian Xu, Bo Zheng

The robust representation learning employs domain adversarial learning and multi-view wasserstein distribution learning to learn robust representations.

Fairness Representation Learning

AdaptDHM: Adaptive Distribution Hierarchical Model for Multi-Domain CTR Prediction

no code implementations • 22 Nov 2022 • Jinyun Li, Huiwen Zheng, Yuanlin Liu, Minfang Lu, Lixia Wu, Haoyuan Hu

Large-scale commercial platforms usually involve numerous business domains for diverse business strategies and expect their recommendation systems to provide click-through rate (CTR) predictions for multiple domains simultaneously.

Click-Through Rate Prediction Recommendation Systems

OPT-GAN: A Broad-Spectrum Global Optimizer for Black-box Problems by Learning Distribution

1 code implementation • 7 Feb 2021 • Minfang Lu, Shuai Ning, Shuangrong Liu, Fengyang Sun, Bo Zhang, Bo Yang, Lin Wang

Black-box optimization (BBO) algorithms are concerned with finding the best solutions for problems with missing analytical details.