activity
20172022
most citedCategory-Specific CNN for Visual-aware CTR Prediction at JD.com

32 citations · 91 across the 11 of their papers we have counts for

collaborators

15 papers

cs.IR20224 cited

Adaptive Experimentation with Delayed Binary Feedback

Zenan Wang, Carlos Carrion, Xiliang Lin +3

Conducting experiments with objectives that take significant delays to materialize (e.g. conversions, add-to-cart events, etc.) is challenging. Although the classical "split sample…

cs.LG2021

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…

cs.CL20211 cited

Probing Product Description Generation via Posterior Distillation

Haolan Zhan, Hainan Zhang, Hongshen Chen +5

In product description generation (PDG), the user-cared aspect is critical for the recommendation system, which can not only improve user's experiences but also obtain more clicks.…

cs.AI20206 cited

DADNN: Multi-Scene CTR Prediction via Domain-Aware Deep Neural Network

Junyou He, Guibao Mei, Feng Xing +3

Click through rate(CTR) prediction is a core task in advertising systems. The booming e-commerce business in our company, results in a growing number of scenes. Most of them are so…

cs.LG2020

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…

cs.LG2020

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…