194 citations · 395 across the 15 of their papers we have counts for
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Towards Understanding the Overfitting Phenomenon of Deep Click-Through Rate Prediction Models
Zhao-Yu Zhang, Xiang-Rong Sheng, Yujing Zhang +4
Deep learning techniques have been applied widely in industrial recommendation systems. However, far less attention has been paid to the overfitting problem of models in recommenda…
KEEP: An Industrial Pre-Training Framework for Online Recommendation via Knowledge Extraction and Plugging
Yujing Zhang, Zhangming Chan, Shuhao Xu +4
An industrial recommender system generally presents a hybrid list that contains results from multiple subsystems. In practice, each subsystem is optimized with its own feedback dat…
Joint Optimization of Ranking and Calibration with Contextualized Hybrid Model
Xiang-Rong Sheng, Jingyue Gao, Yueyao Cheng +6
Despite the development of ranking optimization techniques, pointwise loss remains the dominating approach for click-through rate prediction. It can be attributed to the calibratio…
GBA: A Tuning-free Approach to Switch between Synchronous and Asynchronous Training for Recommendation Model
Wenbo Su, Yuanxing Zhang, Yufeng Cai +9
High-concurrency asynchronous training upon parameter server (PS) architecture and high-performance synchronous training upon all-reduce (AR) architecture are the most commonly dep…
Approximate Nearest Neighbor Search under Neural Similarity Metric for Large-Scale Recommendation
Rihan Chen, Bin Liu, Han Zhu +8
Model-based methods for recommender systems have been studied extensively for years. Modern recommender systems usually resort to 1) representation learning models which define use…