Search Results for author: Kun Gai

Found 34 papers, 15 papers with code

Truncation-Free Matching System for Display Advertising at Alibaba

no code implementations18 Feb 2021 Jin Li, Jie Liu, Shangzhou Li, Yao Xu, Ran Cao, Qi Li, Biye Jiang, Guan Wang, Han Zhu, Kun Gai, Xiaoqiang Zhu

When receiving a user request, matching system (i) finds the crowds that the user belongs to; (ii) retrieves all ads that have targeted those crowds.

TAG

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

no code implementations5 Dec 2020 Zhilin Zhang, Xiangyu Liu, Zhenzhe Zheng, Chenrui Zhang, Miao Xu, Junwei Pan, Chuan Yu, Fan Wu, Jian Xu, Kun Gai

In e-commerce advertising, the ad platform usually relies on auction mechanisms to optimize different performance metrics, such as user experience, advertiser utility, and platform revenue.

Exploration in Online Advertising Systems with Deep Uncertainty-Aware Learning

1 code implementation25 Nov 2020 Chao Du, Zhifeng Gao, Shuo Yuan, Lining Gao, Ziyan Li, Yifan Zeng, Xiaoqiang Zhu, Jian Xu, Kun Gai, Kuang-Chih Lee

In this paper, we propose a novel Deep Uncertainty-Aware Learning (DUAL) method to learn CTR models based on Gaussian processes, which can provide predictive uncertainty estimations while maintaining the flexibility of deep neural networks.

Click-Through Rate Prediction Gaussian Processes

Learning to Infer User Hidden States for Online Sequential Advertising

no code implementations3 Sep 2020 Zhaoqing Peng, Junqi Jin, Lan Luo, Yaodong Yang, Rui Luo, Jun Wang, Wei-Nan Zhang, Haiyang Xu, Miao Xu, Chuan Yu, Tiejian Luo, Han Li, Jian Xu, Kun Gai

To drive purchase in online advertising, it is of the advertiser's great interest to optimize the sequential advertising strategy whose performance and interpretability are both important.

reinforcement-learning

A Deep Prediction Network for Understanding Advertiser Intent and Satisfaction

no code implementations20 Aug 2020 Liyi Guo, Rui Lu, Haoqi Zhang, Junqi Jin, Zhenzhe Zheng, Fan Wu, Jin Li, Haiyang Xu, Han Li, Wenkai Lu, Jian Xu, Kun Gai

For e-commerce platforms such as Taobao and Amazon, advertisers play an important role in the entire digital ecosystem: their behaviors explicitly influence users' browsing and shopping experience; more importantly, advertiser's expenditure on advertising constitutes a primary source of platform revenue.

Learning Optimal Tree Models Under Beam Search

1 code implementation ICML 2020 Jingwei Zhuo, Ziru Xu, Wei Dai, Han Zhu, Han Li, Jian Xu, Kun Gai

Retrieving relevant targets from an extremely large target set under computational limits is a common challenge for information retrieval and recommendation systems.

Information Retrieval Recommendation Systems

DCAF: A Dynamic Computation Allocation Framework for Online Serving System

no code implementations17 Jun 2020 Biye Jiang, Pengye Zhang, Rihan Chen, Binding Dai, Xinchen Luo, Yin Yang, Guan Wang, Guorui Zhou, Xiaoqiang Zhu, Kun Gai

These stages usually allocate resource manually with specific computing power budgets, which requires the serving configuration to adapt accordingly.

Recommendation Systems

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

no code implementations9 May 2020 Xiaotian Hao, Junqi Jin, Jianye Hao, Jin Li, Weixun Wang, Yi Ma, Zhenzhe Zheng, Han Li, Jian Xu, Kun Gai

Bipartite b-matching is fundamental in algorithm design, and has been widely applied into economic markets, labor markets, etc.

A Deep Recurrent Survival Model for Unbiased Ranking

1 code implementation30 Apr 2020 Jiarui Jin, Yuchen Fang, Wei-Nan Zhang, Kan Ren, Guorui Zhou, Jian Xu, Yong Yu, Jun Wang, Xiaoqiang Zhu, Kun Gai

Position bias is a critical problem in information retrieval when dealing with implicit yet biased user feedback data.

Information Retrieval Survival Analysis

Stick to the Facts: Learning towards a Fidelity-oriented E-Commerce Product Description Generation

no code implementations IJCNLP 2019 Zhangming Chan, Xiuying Chen, Yongliang Wang, Juntao Li, Zhiqiang Zhang, Kun Gai, Dongyan Zhao, Rui Yan

Different from other text generation tasks, in product description generation, it is of vital importance to generate faithful descriptions that stick to the product attribute information.

Text Generation

Res-embedding for Deep Learning Based Click-Through Rate Prediction Modeling

no code implementations25 Jun 2019 Guorui Zhou, Kailun Wu, Weijie Bian, Zhao Yang, Xiaoqiang Zhu, Kun Gai

In this paper, we model user behavior using an interest delay model, study carefully the embedding mechanism, and obtain two important results: (i) We theoretically prove that small aggregation radius of embedding vectors of items which belongs to a same user interest domain will result in good generalization performance of deep CTR model.

Click-Through Rate Prediction

Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction

2 code implementations22 May 2019 Qi Pi, Weijie Bian, Guorui Zhou, Xiaoqiang Zhu, Kun Gai

To our knowledge, this is one of the first industrial solutions that are capable of handling long sequential user behavior data with length scaling up to thousands.

Click-Through Rate Prediction Recommendation Systems

Lifelong Sequential Modeling with Personalized Memorization for User Response Prediction

1 code implementation2 May 2019 Kan Ren, Jiarui Qin, Yuchen Fang, Wei-Nan Zhang, Lei Zheng, Weijie Bian, Guorui Zhou, Jian Xu, Yong Yu, Xiaoqiang Zhu, Kun Gai

In order to tackle these challenges, in this paper, we propose a Hierarchical Periodic Memory Network for lifelong sequential modeling with personalized memorization of sequential patterns for each user.

Improve Diverse Text Generation by Self Labeling Conditional Variational Auto Encoder

no code implementations26 Mar 2019 Yuchi Zhang, Yongliang Wang, Liping Zhang, Zhiqiang Zhang, Kun Gai

In fact, this objective term guides the encoder towards the "best encoder" of the decoder to enhance the expressiveness.

Text Generation

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

1 code implementation NeurIPS 2019 Han Zhu, Daqing Chang, Ziru Xu, Pengye Zhang, Xiang Li, Jie He, Han Li, Jian Xu, Kun Gai

The previous work Tree-based Deep Model (TDM) \cite{zhu2018learning} greatly improves recommendation accuracy using tree index.

Recommendation Systems

Deep Interest Evolution Network for Click-Through Rate Prediction

13 code implementations11 Sep 2018 Guorui Zhou, Na Mou, Ying Fan, Qi Pi, Weijie Bian, Chang Zhou, Xiaoqiang Zhu, Kun Gai

Easy-to-use, Modular and Extendible package of deep-learning based CTR models. DeepFM, DeepInterestNetwork(DIN), DeepInterestEvolutionNetwork(DIEN), DeepCrossNetwork(DCN), AttentionalFactorizationMachine(AFM), Neural Factorization Machine(NFM), AutoInt

Click-Through Rate Prediction

Learning Adaptive Display Exposure for Real-Time Advertising

no code implementations10 Sep 2018 Weixun Wang, Junqi Jin, Jianye Hao, Chunjie Chen, Chuan Yu, Wei-Nan Zhang, Jun Wang, Xiaotian Hao, Yixi Wang, Han Li, Jian Xu, Kun Gai

In this paper, we investigate the problem of advertising with adaptive exposure: can we dynamically determine the number and positions of ads for each user visit under certain business constraints so that the platform revenue can be increased?

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

no code implementations10 Sep 2018 Di Wu, Cheng Chen, Xun Yang, Xiujun Chen, Qing Tan, Jian Xu, Kun Gai

With this formulation, we derive the optimal impression allocation strategy by solving the optimal bidding functions for contracts.

Multi-agent Reinforcement Learning reinforcement-learning

Semantic Human Matting

2 code implementations5 Sep 2018 Quan Chen, Tiezheng Ge, Yanyu Xu, Zhiqiang Zhang, Xinxin Yang, Kun Gai

SHM is the first algorithm that learns to jointly fit both semantic information and high quality details with deep networks.

Image Matting

Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate

4 code implementations21 Apr 2018 Xiao Ma, Liqin Zhao, Guan Huang, Zhi Wang, Zelin Hu, Xiaoqiang Zhu, Kun Gai

To the best of our knowledge, this is the first public dataset which contains samples with sequential dependence of click and conversion labels for CVR modeling.

Click-Through Rate Prediction Recommendation Systems +2

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

no code implementations23 Feb 2018 Di Wu, Xiujun Chen, Xun Yang, Hao Wang, Qing Tan, Xiaoxun Zhang, Jian Xu, Kun Gai

Our analysis shows that the immediate reward from environment is misleading under a critical resource constraint.

reinforcement-learning

Learning Tree-based Deep Model for Recommender Systems

4 code implementations8 Jan 2018 Han Zhu, Xiang Li, Pengye Zhang, Guozheng Li, Jie He, Han Li, Kun Gai

In systems with large corpus, however, the calculation cost for the learnt model to predict all user-item preferences is tremendous, which makes full corpus retrieval extremely difficult.

Recommendation Systems

Rocket Launching: A Universal and Efficient Framework for Training Well-performing Light Net

1 code implementation14 Aug 2017 Guorui Zhou, Ying Fan, Runpeng Cui, Weijie Bian, Xiaoqiang Zhu, Kun Gai

Models applied on real time response task, like click-through rate (CTR) prediction model, require high accuracy and rigorous response time.

Click-Through Rate Prediction

Deep Interest Network for Click-Through Rate Prediction

16 code implementations21 Jun 2017 Guorui Zhou, Chengru Song, Xiaoqiang Zhu, Ying Fan, Han Zhu, Xiao Ma, Yanghui Yan, Junqi Jin, Han Li, Kun Gai

In this way, user features are compressed into a fixed-length representation vector, in regardless of what candidate ads are.

Click-Through Rate Prediction

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

2 code implementations18 Apr 2017 Kun Gai, Xiaoqiang Zhu, Han Li, Kai Liu, Zhe Wang

CTR prediction in real-world business is a difficult machine learning problem with large scale nonlinear sparse data.

Click-Through Rate Prediction Feature Engineering

Optimized Cost per Click in Taobao Display Advertising

no code implementations27 Feb 2017 Han Zhu, Junqi Jin, Chang Tan, Fei Pan, Yifan Zeng, Han Li, Kun Gai

Moreover, the platform has to be responsible for the business revenue and user experience.

Learning Kernels with Radiuses of Minimum Enclosing Balls

no code implementations NeurIPS 2010 Kun Gai, Guangyun Chen, Chang-Shui Zhang

Experiments show that our method significantly outperforms both SVM with the uniform combination of basis kernels and other state-of-art MKL approaches.

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