Chang Zhou

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Harbin Institute of Technology ∙ University of California, San Diego

Contrastive Learning for Debiased Candidate Generation in Large-Scale Recommender Systems

Deep candidate generation (DCG) that narrows down the collection of rele...
05/20/2020 ∙ by Chang Zhou, et al. ∙ 0 ∙ share

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Contrastive Learning for Debiased Candidate Generation at Scale

Deep candidate generation has become an increasingly popular choice depl...
05/20/2020 ∙ by Chang Zhou, et al. ∙ 0 ∙ share

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Understanding Negative Sampling in Graph Representation Learning

Graph representation learning has been extensively studied in recent yea...
05/20/2020 ∙ by Zhen Yang, et al. ∙ 24 ∙ share

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Controllable Multi-Interest Framework for Recommendation

Recently, neural networks have been widely used in e-commerce recommende...
05/19/2020 ∙ by Yukuo Cen, et al. ∙ 0 ∙ share

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ExperienceThinking: Hyperparameter Optimization with Budget Constraints

The problem of hyperparameter optimization exists widely in the real lif...
12/02/2019 ∙ by Chunnan Wang, et al. ∙ 0 ∙ share

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Learning Disentangled Representations for Recommendation

User behavior data in recommender systems are driven by the complex inte...
10/31/2019 ∙ by Jianxin Ma, et al. ∙ 0 ∙ share

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Dimensional Reweighting Graph Convolutional Networks

Graph Convolution Networks (GCNs) are becoming more and more popular for...
07/04/2019 ∙ by Xu Zou, et al. ∙ 0 ∙ share

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Cognitive Knowledge Graph Reasoning for One-shot Relational Learning

Inferring new facts from existing knowledge graphs (KG) with explainable...
06/13/2019 ∙ by Zhengxiao Du, et al. ∙ 0 ∙ share

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Cognitive Graph for Multi-Hop Reading Comprehension at Scale

We propose a new CogQA framework for multi-hop question answering in web...
05/14/2019 ∙ by Ming Ding, et al. ∙ 0 ∙ share

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Personalized Bundle List Recommendation

Product bundling, offering a combination of items to customers, is one o...
04/03/2019 ∙ by Jinze Bai, et al. ∙ 0 ∙ share

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AliGraph: A Comprehensive Graph Neural Network Platform

An increasing number of machine learning tasks require dealing with larg...
02/23/2019 ∙ by Rong Zhu, et al. ∙ 0 ∙ share

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Deep Interest Evolution Network for Click-Through Rate Prediction

Click-through rate (CTR) prediction, whose goal is to estimate the proba...
09/11/2018 ∙ by Guorui Zhou, et al. ∙ 0 ∙ share

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ATRank: An Attention-Based User Behavior Modeling Framework for Recommendation

A user can be represented as what he/she does along the history. A commo...
11/17/2017 ∙ by Chang Zhou, et al. ∙ 0 ∙ share

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Chang Zhou

Featured Co-authors

Jie Tang

Ming Ding

Yong Li

Hongzhi Wang

Kun Gai

Wei Lin

Peng Cui

Hongxia Yang

Jingren Zhou

Wenwu Zhu

Jianzhong Li

Contrastive Learning for Debiased Candidate Generation in Large-Scale Recommender Systems

Contrastive Learning for Debiased Candidate Generation at Scale

Understanding Negative Sampling in Graph Representation Learning

Controllable Multi-Interest Framework for Recommendation

ExperienceThinking: Hyperparameter Optimization with Budget Constraints

Learning Disentangled Representations for Recommendation

Dimensional Reweighting Graph Convolutional Networks

Cognitive Knowledge Graph Reasoning for One-shot Relational Learning

Cognitive Graph for Multi-Hop Reading Comprehension at Scale

Personalized Bundle List Recommendation

AliGraph: A Comprehensive Graph Neural Network Platform

Deep Interest Evolution Network for Click-Through Rate Prediction

ATRank: An Attention-Based User Behavior Modeling Framework for Recommendation