32 citations · 110 across the 17 of their papers we have counts for
8 papers · 1 filter
Blending Advertising with Organic Content in E-Commerce: A Virtual Bids Optimization Approach
Carlos Carrion, Zenan Wang, Harikesh Nair +8
In e-commerce platforms, sponsored and non-sponsored content are jointly displayed to users and both may interactively influence their engagement behavior. The former content helps…
BERT2DNN: BERT Distillation with Massive Unlabeled Data for Online E-Commerce Search
Yunjiang Jiang, Yue Shang, Ziyang Liu +6
Relevance has significant impact on user experience and business profit for e-commerce search platform. In this work, we propose a data-driven framework for search relevance predic…
Kalman Filtering Attention for User Behavior Modeling in CTR Prediction
Hu Liu, Jing Lu, Xiwei Zhao +8
Click-through rate (CTR) prediction is one of the fundamental tasks for e-commerce search engines. As search becomes more personalized, it is necessary to capture the user interest…
Adversarial Mixture Of Experts with Category Hierarchy Soft Constraint
Zhuojian Xiao, Yunjiang jiang, Guoyu Tang +4
Product search is the most common way for people to satisfy their shopping needs on e-commerce websites. Products are typically annotated with one of several broad categorical tags…
Category-Specific CNN for Visual-aware CTR Prediction at JD.com
Hu Liu, Jing Lu, Hao Yang +8
As one of the largest B2C e-commerce platforms in China, JD com also powers a leading advertising system, serving millions of advertisers with fingertip connection to hundreds of m…
Regularized Adversarial Sampling and Deep Time-aware Attention for Click-Through Rate Prediction
Yikai Wang, Liang Zhang, Quanyu Dai +5
Improving the performance of click-through rate (CTR) prediction remains one of the core tasks in online advertising systems. With the rise of deep learning, CTR prediction models…
Deep Hierarchical Reinforcement Learning Based Recommendations via Multi-goals Abstraction
Dongyang Zhao, Liang Zhang, Bo Zhang +3
The recommender system is an important form of intelligent application, which assists users to alleviate from information redundancy. Among the metrics used to evaluate a recommend…
LADDER: A Human-Level Bidding Agent for Large-Scale Real-Time Online Auctions
Yu Wang, Jiayi Liu, Yuxiang Liu +5
We present LADDER, the first deep reinforcement learning agent that can successfully learn control policies for large-scale real-world problems directly from raw inputs composed of…