research
∙
09/22/2021
Context-aware Tree-based Deep Model for Recommender Systems
How to predict precise user preference and how to make efficient retriev...
share
research
∙
06/11/2021
A Cooperative-Competitive Multi-Agent Framework for Auto-bidding in Online Advertising
In online advertising, auto-bidding has become an essential tool for adv...
research
∙
06/07/2021
Neural Auction: End-to-End Learning of Auction Mechanisms for E-Commerce Advertising
In e-commerce advertising, it is crucial to jointly consider various per...
research
∙
05/25/2021
We Know What You Want: An Advertising Strategy Recommender System for Online Advertising
Advertising expenditures have become the major source of revenue for e-c...
research
∙
04/29/2021
Real Negatives Matter: Continuous Training with Real Negatives for Delayed Feedback Modeling
One of the difficulties of conversion rate (CVR) prediction is that the ...
research
∙
03/03/2021
Computation Resource Allocation Solution in Recommender Systems
Recommender systems rely heavily on increasing computation resources to ...
research
∙
02/18/2021
Truncation-Free Matching System for Display Advertising at Alibaba
Matching module plays a critical role in display advertising systems. Wi...
research
∙
01/27/2021
One Model to Serve All: Star Topology Adaptive Recommender for Multi-Domain CTR Prediction
Traditional industrial recommenders are usually trained on a single busi...
research
∙
11/25/2020
Exploration in Online Advertising Systems with Deep Uncertainty-Aware Learning
Modern online advertising systems inevitably rely on personalization met...
research
∙
11/11/2020
CAN: Revisiting Feature Co-Action for Click-Through Rate Prediction
Inspired by the success of deep learning, recent industrial Click-Throug...
research
∙
07/31/2020
COLD: Towards the Next Generation of Pre-Ranking System
Multi-stage cascade architecture exists widely in many industrial system...
research
∙
06/17/2020
DCAF: A Dynamic Computation Allocation Framework for Online Serving System
Modern large-scale systems such as recommender system and online adverti...
research
∙
06/10/2020
Search-based User Interest Modeling with Lifelong Sequential Behavior Data for Click-Through Rate Prediction
Rich user behavior data has been proven to be of great value for click-t...
research
∙
07/12/2019
AMAD: Adversarial Multiscale Anomaly Detection on High-Dimensional and Time-Evolving Categorical Data
Anomaly detection is facing with emerging challenges in many important i...
research
∙
06/25/2019
Res-embedding for Deep Learning Based Click-Through Rate Prediction Modeling
Recently, click-through rate (CTR) prediction models have evolved from s...
research
∙
05/22/2019
Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction
Click-through rate (CTR) prediction is critical for industrial applicati...
research
∙
05/02/2019
Lifelong Sequential Modeling with Personalized Memorization for User Response Prediction
User response prediction, which models the user preference w.r.t. the pr...
research
∙
09/11/2018
Deep Interest Evolution Network for Click-Through Rate Prediction
Click-through rate (CTR) prediction, whose goal is to estimate the proba...
research
∙
04/21/2018
Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate
Estimating post-click conversion rate (CVR) accurately is crucial for ra...
research
∙
11/17/2017
Image Matters: Jointly Train Advertising CTR Model with Image Representation of Ad and User Behavior
Click Through Rate(CTR) prediction is vital for online advertising syste...
research
∙
08/14/2017
Rocket Launching: A Universal and Efficient Framework for Training Well-performing Light Net
Models applied on real time response task, like click-through rate (CTR)...
research
∙
06/21/2017
Deep Interest Network for Click-Through Rate Prediction
To better extract users' interest by exploiting the rich historical beha...
research
∙
04/18/2017
