b'Kun Gai'

research

∙ 08/11/2023

A Large Language Model Enhanced Conversational Recommender System

Conversational recommender systems (CRSs) aim to recommend high-quality ...

0 Yue Feng, et al. ∙

research

∙ 06/06/2023

State Regularized Policy Optimization on Data with Dynamics Shift

In many real-world scenarios, Reinforcement Learning (RL) algorithms are...

0 Zhenghai Xue, et al. ∙

research

∙ 06/04/2023

Generative Flow Network for Listwise Recommendation

Personalized recommender systems fulfill the daily demands of customers ...

0 Shuchang Liu, et al. ∙

research

∙ 02/07/2023

Multi-Task Recommendations with Reinforcement Learning

In recent years, Multi-task Learning (MTL) has yielded immense success i...

0 Ziru Liu, et al. ∙

research

∙ 02/07/2023

Disentangled Causal Embedding With Contrastive Learning For Recommender System

Recommender systems usually rely on observed user interaction data to bu...

0 Weiqi Zhao, et al. ∙

research

∙ 02/03/2023

Reinforcing User Retention in a Billion Scale Short Video Recommender System

Recently, short video platforms have achieved rapid user growth by recom...

0 Qingpeng Cai, et al. ∙

research

∙ 02/03/2023

Two-Stage Constrained Actor-Critic for Short Video Recommendation

The wide popularity of short videos on social media poses new opportunit...

0 Qingpeng Cai, et al. ∙

research

∙ 02/18/2021

Truncation-Free Matching System for Display Advertising at Alibaba

Matching module plays a critical role in display advertising systems. Wi...

0 Jin Li, et al. ∙

research

∙ 12/05/2020

Optimizing Multiple Performance Metrics with Deep GSP Auctions for E-commerce Advertising

In e-commerce advertising, the ad platform usually relies on auction mec...

0 Zhilin Zhang, et al. ∙

research

∙ 11/25/2020

Exploration in Online Advertising Systems with Deep Uncertainty-Aware Learning

Modern online advertising systems inevitably rely on personalization met...

0 Chao Du, et al. ∙

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...

0 Guorui Zhou, et al. ∙

research

∙ 09/03/2020

Learning to Infer User Hidden States for Online Sequential Advertising

To drive purchase in online advertising, it is of the advertiser's great...

8 Zhaoqing Peng, et al. ∙

research

∙ 08/20/2020

A Deep Prediction Network for Understanding Advertiser Intent and Satisfaction

For e-commerce platforms such as Taobao and Amazon, advertisers play an ...

9 Liyi Guo, et al. ∙

research

∙ 07/31/2020

COLD: Towards the Next Generation of Pre-Ranking System

Multi-stage cascade architecture exists widely in many industrial system...

13 Zhe Wang, et al. ∙

research

∙ 06/29/2020

Dynamic Knapsack Optimization Towards Efficient Multi-Channel Sequential Advertising

In E-commerce, advertising is essential for merchants to reach their tar...

8 Xiaotian Hao, et al. ∙

research

∙ 06/27/2020

Learning Optimal Tree Models Under Beam Search

Retrieving relevant targets from an extremely large target set under com...

0 Jingwei Zhuo, et al. ∙

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...

0 Biye Jiang, et al. ∙

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...

0 Pi Qi, et al. ∙

research

∙ 05/09/2020

Learning to Accelerate Heuristic Searching for Large-Scale Maximum Weighted b-Matching Problems in Online Advertising

Bipartite b-matching is fundamental in algorithm design, and has been wi...

0 Xiaotian Hao, et al. ∙

research

∙ 11/14/2019

Progressive Feature Polishing Network for Salient Object Detection

Feature matters for salient object detection. Existing methods mainly fo...

22 Bo Wang, et al. ∙

research

∙ 08/19/2019

Learning to Advertise for Organic Traffic Maximization in E-Commerce Product Feeds

Most e-commerce product feeds provide blended results of advertised prod...

0 Dagui Chen, et al. ∙

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...

0 Guorui Zhou, et al. ∙

research

∙ 05/24/2019

Bid Optimization by Multivariable Control in Display Advertising

Real-Time Bidding (RTB) is an important paradigm in display advertising,...

0 Xun Yang, et al. ∙

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...

0 Qi Pi, et al. ∙

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...

0 Kan Ren, et al. ∙

research

∙ 03/26/2019

Improve Diverse Text Generation by Self Labeling Conditional Variational Auto Encoder

Diversity plays a vital role in many text generating applications. In re...

0 Yuchi Zhang, et al. ∙

research

∙ 02/19/2019

Joint Optimization of Tree-based Index and Deep Model for Recommender Systems

Large-scale industrial recommender systems are usually confronted with c...

0 Han Zhu, et al. ∙

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...

0 Guorui Zhou, et al. ∙

research

∙ 09/10/2018

A Multi-Agent Reinforcement Learning Method for Impression Allocation in Online Display Advertising

In online display advertising, guaranteed contracts and real-time biddin...

0 Di Wu, et al. ∙

research

∙ 09/10/2018

Learning to Advertise with Adaptive Exposure via Constrained Two-Level Reinforcement Learning

For online advertising in e-commerce, the traditional problem is to assi...

0 Weixun Wang, et al. ∙

research

∙ 09/05/2018

Semantic Human Matting

Human matting, high quality extraction of humans from natural images, is...

0 Quan Chen, et al. ∙

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...

0 Xiao Ma, et al. ∙

research

∙ 02/27/2018

Real-Time Bidding with Multi-Agent Reinforcement Learning in Display Advertising

Real-time advertising allows advertisers to bid for each impression for ...

0 Junqi Jin, et al. ∙

research

∙ 02/23/2018

Budget Constrained Bidding by Model-free Reinforcement Learning in Display Advertising

Real-time bidding (RTB) is almost the most important mechanism in online...

0 Di Wu, et al. ∙

research

∙ 01/08/2018

Learning Tree-based Deep Model for Recommender Systems

We propose a novel recommendation method based on tree. With user behavi...

0 Han Zhu, et al. ∙

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...

0 Tiezheng Ge, et al. ∙

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)...

0 Guorui Zhou, et al. ∙

research

∙ 06/21/2017

Deep Interest Network for Click-Through Rate Prediction

To better extract users' interest by exploiting the rich historical beha...

0 Guorui Zhou, et al. ∙

research

∙ 04/18/2017

Learning Piece-wise Linear Models from Large Scale Data for Ad Click Prediction

CTR prediction in real-world business is a difficult machine learning pr...

0 Kun Gai, et al. ∙

research

∙ 02/27/2017

Optimized Cost per Click in Taobao Display Advertising

Taobao, as the largest online retail platform in the world, provides bil...

0 Han Zhu, et al. ∙

research

∙ 11/19/2012

Efficient Superimposition Recovering Algorithm

In this article, we address the issue of recovering latent transparent l...

0 Han Li, et al. ∙

Kun Gai

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