17 citations · 38 across the 8 of their papers we have counts for
8 papers
Rethinking Large-scale Pre-ranking System: Entire-chain Cross-domain Models
Jinbo Song, Ruoran Huang, Xinyang Wang +9
Industrial systems such as recommender systems and online advertising, have been widely equipped with multi-stage architectures, which are divided into several cascaded modules, in…
Towards Better Query Classification with Multi-Expert Knowledge Condensation in JD Ads Search
Kun-Peng Ning, Ming Pang, Zheng Fang +6
Search query classification, as an effective way to understand user intents, is of great importance in real-world online ads systems. To ensure a lower latency, a shallow model (e.…
Always Strengthen Your Strengths: A Drift-Aware Incremental Learning Framework for CTR Prediction
Congcong Liu, Fei Teng, Xiwei Zhao +3
Click-through rate (CTR) prediction is of great importance in recommendation systems and online advertising platforms. When served in industrial scenarios, the user-generated data…
PCDF: A Parallel-Computing Distributed Framework for Sponsored Search Advertising Serving
Han Xu, Hao Qi, Kunyao Wang +8
Traditional online advertising systems for sponsored search follow a cascade paradigm with retrieval, pre-ranking,ranking, respectively. Constrained by strict requirements on onlin…
On the Adaptation to Concept Drift for CTR Prediction
Congcong Liu, Yuejiang Li, Fei Teng +5
Click-through rate (CTR) prediction is a crucial task in web search, recommender systems, and online advertisement displaying. In practical application, CTR models often serve with…
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…