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8 papers · 2 filters
Stagewise Training Accelerates Convergence of Testing Error Over SGD
Zhuoning Yuan, Yan Yan, Rong Jin +1
Stagewise training strategy is widely used for learning neural networks, which runs a stochastic algorithm (e.g., SGD) starting with a relatively large step size (aka learning rate…
Deep Interest Evolution Network for Click-Through Rate Prediction
Guorui Zhou, Na Mou, Ying Fan +5
Click-through rate~(CTR) prediction, whose goal is to estimate the probability of the user clicks, has become one of the core tasks in advertising systems. For CTR prediction model…
Visualizing and Understanding Deep Neural Networks in CTR Prediction
Lin Guo, Hui Ye, Wenbo Su +3
Although deep learning techniques have been successfully applied to many tasks, interpreting deep neural network models is still a big challenge to us. Recently, many works have be…
Perceive Your Users in Depth: Learning Universal User Representations from Multiple E-commerce Tasks
Yabo Ni, Dan Ou, Shichen Liu +4
Tasks such as search and recommendation have become increas- ingly important for E-commerce to deal with the information over- load problem. To meet the diverse needs of di erent u…
Fast Rates of ERM and Stochastic Approximation: Adaptive to Error Bound Conditions
Mingrui Liu, Xiaoxuan Zhang, Lijun Zhang +2
Error bound conditions (EBC) are properties that characterize the growth of an objective function when a point is moved away from the optimal set. They have recently received incre…
Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate
Xiao Ma, Liqin Zhao, Guan Huang +4
Estimating post-click conversion rate (CVR) accurately is crucial for ranking systems in industrial applications such as recommendation and advertising. Conventional CVR modeling a…