32 citations · 110 across the 17 of their papers we have counts for
17 papers
Learning Multi-Stage Multi-Grained Semantic Embeddings for E-Commerce Search
Binbin Wang, Mingming Li, Zhixiong Zeng +5
Retrieving relevant items that match users' queries from billion-scale corpus forms the core of industrial e-commerce search systems, in which embedding-based retrieval (EBR) metho…
Exploring Structured Semantic Prior for Multi Label Recognition with Incomplete Labels
Zixuan Ding, Ao Wang, Hui Chen +5
Multi-label recognition (MLR) with incomplete labels is very challenging. Recent works strive to explore the image-to-label correspondence in the vision-language model, \ie, CLIP,…
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…
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…
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.…
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…
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…
Group-wise Contrastive Learning for Neural Dialogue Generation
Hengyi Cai, Hongshen Chen, Yonghao Song +4
Neural dialogue response generation has gained much popularity in recent years. Maximum Likelihood Estimation (MLE) objective is widely adopted in existing dialogue model learning.…
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…
Towards Personalized and Semantic Retrieval: An End-to-End Solution for E-commerce Search via Embedding Learning
Han Zhang, Songlin Wang, Kang Zhang +5
Nowadays e-commerce search has become an integral part of many people's shopping routines. Two critical challenges stay in today's e-commerce search: how to retrieve items that are…
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…
Telepath: Understanding Users from a Human Vision Perspective in Large-Scale Recommender Systems
Yu Wang, Jixing Xu, Aohan Wu +4
Designing an e-commerce recommender system that serves hundreds of millions of active users is a daunting challenge. From a human vision perspective, there're two key factors that…
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…
Optimizing Gross Merchandise Volume via DNN-MAB Dynamic Ranking Paradigm
Yan Yan, Wentao Guo, Meng Zhao +2
With the transition from people's traditional `brick-and-mortar' shopping to online mobile shopping patterns in web 2.0 , the recommender system plays a critical role…