4 citations · 6 across the 7 of their papers we have counts for
7 papers
Compress, Cross and Scale: Multi-Level Compression Cross Networks for Efficient Scaling in Recommender Systems
Heng Yu, Xiangjun Zhou, Jie Xia +4
Modeling high-order feature interactions efficiently is a central challenge in click-through rate and conversion rate prediction. Modern industrial recommender systems are predomin…
SPICE: Submodular Penalized Information-Conflict Selection for Efficient Large Language Model Training
Powei Chang, Jinpeng Zhang, Bowen Chen +9
Information-based data selection for instruction tuning is compelling: maximizing the log-determinant of the Fisher information yields a monotone submodular objective, enabling gre…
Multi-Epoch Learning for Deep Click-Through Rate Prediction Models
Zhaocheng Liu, Zhongxiang Fan, Jian Liang +2
The one-epoch overfitting phenomenon has been widely observed in industrial Click-Through Rate (CTR) applications, where the model performance experiences a significant degradation…
Improving Multi-Interest Network with Stable Learning
Zhaocheng Liu, Yingtao Luo, Di Zeng +4
Modeling users' dynamic preferences from historical behaviors lies at the core of modern recommender systems. Due to the diverse nature of user interests, recent advances propose t…
LPFS: Learnable Polarizing Feature Selection for Click-Through Rate Prediction
Yi Guo, Zhaocheng Liu, Jianchao Tan +6
In industry, feature selection is a standard but necessary step to search for an optimal set of informative feature fields for efficient and effective training of deep Click-Throug…
Persia: An Open, Hybrid System Scaling Deep Learning-based Recommenders up to 100 Trillion Parameters
Xiangru Lian, Binhang Yuan, Xuefeng Zhu +24
Deep learning based models have dominated the current landscape of production recommender systems. Furthermore, recent years have witnessed an exponential growth of the model scale…
PASTO: Strategic Parameter Optimization in Recommendation Systems -- Probabilistic is Better than Deterministic
Weicong Ding, Hanlin Tang, Jingshuo Feng +17
Real-world recommendation systems often consist of two phases. In the first phase, multiple predictive models produce the probability of different immediate user actions. In the se…