b'Xiuqiang He'

research

∙ 06/23/2023

OptMSM: Optimizing Multi-Scenario Modeling for Click-Through Rate Prediction

A large-scale industrial recommendation platform typically consists of m...

0 Xing Tang, et al. ∙

research

∙ 06/01/2023

Explicit Feature Interaction-aware Uplift Network for Online Marketing

As a key component in online marketing, uplift modeling aims to accurate...

0 Dugang Liu, et al. ∙

research

∙ 04/25/2023

Curriculum Modeling the Dependence among Targets with Multi-task Learning for Financial Marketing

Multi-task learning for various real-world applications usually involves...

0 Yunpeng Weng, et al. ∙

research

∙ 02/07/2023

Self-Sampling Training and Evaluation for the Accuracy-Bias Tradeoff in Recommendation

Research on debiased recommendation has shown promising results. However...

0 Dugang Liu, et al. ∙

research

∙ 02/04/2023

Feature Representation Learning for Click-through Rate Prediction: A Review and New Perspectives

Representation learning has been a critical topic in machine learning. I...

0 Fuyuan Lyu, et al. ∙

research

∙ 01/26/2023

Optimizing Feature Set for Click-Through Rate Prediction

Click-through prediction (CTR) models transform features into latent vec...

0 Fuyuan Lyu, et al. ∙

research

∙ 07/06/2022

DIWIFT: Discovering Instance-wise Influential Features for Tabular Data

Tabular data is one of the most common data storage formats in business ...

0 Pengxiang Cheng, et al. ∙

research

∙ 06/17/2022

A Graph-Enhanced Click Model for Web Search

To better exploit search logs and model users' behavior patterns, numero...

0 Jianghao Lin, et al. ∙

research

∙ 03/23/2022

PEAR: Personalized Re-ranking with Contextualized Transformer for Recommendation

The goal of recommender systems is to provide ordered item lists to user...

0 Yi Li, et al. ∙

research

∙ 12/03/2021

Towards Low-loss 1-bit Quantization of User-item Representations for Top-K Recommendation

Due to the promising advantages in space compression and inference accel...

0 Yankai Chen, et al. ∙

research

∙ 11/16/2021

QA4PRF: A Question Answering based Framework for Pseudo Relevance Feedback

Pseudo relevance feedback (PRF) automatically performs query expansion b...

0 Handong Ma, et al. ∙

research

∙ 11/05/2021

AIM: Automatic Interaction Machine for Click-Through Rate Prediction

Feature embedding learning and feature interaction modeling are two cruc...

0 Chenxu Zhu, et al. ∙

research

∙ 10/28/2021

Cross-Batch Negative Sampling for Training Two-Tower Recommenders

The two-tower architecture has been widely applied for learning item and...

0 Jinpeng Wang, et al. ∙

research

∙ 10/28/2021

UltraGCN: Ultra Simplification of Graph Convolutional Networks for Recommendation

With the recent success of graph convolutional networks (GCNs), they hav...

0 Kelong Mao, et al. ∙

research

∙ 10/25/2021

Content Filtering Enriched GNN Framework for News Recommendation

Learning accurate users and news representations is critical for news re...

0 Yong Gao, et al. ∙

research

∙ 10/18/2021

Context-aware Reranking with Utility Maximization for Recommendation

As a critical task for large-scale commercial recommender systems, reran...

0 Yunjia Xi, et al. ∙

research

∙ 09/26/2021

Why Do We Click: Visual Impression-aware News Recommendation

There is a soaring interest in the news recommendation research scenario...

0 Jiahao Xun, et al. ∙

research

∙ 09/26/2021

SimpleX: A Simple and Strong Baseline for Collaborative Filtering

Collaborative filtering (CF) is a widely studied research topic in recom...

0 Kelong Mao, et al. ∙

research

∙ 08/11/2021

Retrieval Interaction Machine for Tabular Data Prediction

Prediction over tabular data is an essential task in many data science a...

0 Jiarui Qin, et al. ∙

research

∙ 06/09/2021

AutoFT: Automatic Fine-Tune for Parameters Transfer Learning in Click-Through Rate Prediction

Recommender systems are often asked to serve multiple recommendation sce...

0 Xiangli Yang, et al. ∙

research

∙ 06/01/2021

Dual Graph enhanced Embedding Neural Network for CTR Prediction

CTR prediction, which aims to estimate the probability that a user will ...

0 Wei Guo, et al. ∙

research

∙ 04/21/2021

Deep Learning for Click-Through Rate Estimation

Click-through rate (CTR) estimation plays as a core function module in v...

0 Weinan Zhang, et al. ∙

research

∙ 04/17/2021

ScaleFreeCTR: MixCache-based Distributed Training System for CTR Models with Huge Embedding Table

Because of the superior feature representation ability of deep learning,...

0 Huifeng Guo, et al. ∙

research

∙ 04/13/2021

An Adversarial Imitation Click Model for Information Retrieval

Modern information retrieval systems, including web search, ads placemen...

0 Xinyi Dai, et al. ∙

research

∙ 04/01/2021

Modeling High-order Interactions across Multi-interests for Micro-video Recommendation

Personalized recommendation system has become pervasive in various video...

0 Dong Yao, et al. ∙

research

∙ 12/16/2020

AutoDis: Automatic Discretization for Embedding Numerical Features in CTR Prediction

Learning sophisticated feature interactions is crucial for Click-Through...

0 Huifeng Guo, et al. ∙

research

∙ 11/08/2020

Ensembled CTR Prediction via Knowledge Distillation

Recently, deep learning-based models have been widely studied for click-...

0 Jieming Zhu, et al. ∙

research

∙ 11/01/2020

U-rank: Utility-oriented Learning to Rank with Implicit Feedback

Learning to rank with implicit feedback is one of the most important tas...

0 Xinyi Dai, et al. ∙

research

∙ 09/12/2020

FuxiCTR: An Open Benchmark for Click-Through Rate Prediction

In many applications, such as recommender systems, online advertising, a...

6 Jieming Zhu, et al. ∙

research

∙ 09/04/2020

A Practical Incremental Method to Train Deep CTR Models

Deep learning models in recommender systems are usually trained in the b...

0 Yichao Wang, et al. ∙

research

∙ 08/26/2020

Item Tagging for Information Retrieval: A Tripartite Graph Neural Network based Approach

Tagging has been recognized as a successful practice to boost relevance ...

0 Kelong Mao, et al. ∙

research

∙ 08/19/2020

Regularized Two-Branch Proposal Networks for Weakly-Supervised Moment Retrieval in Videos

Video moment retrieval aims to localize the target moment in an video ac...

0 Zhu Zhang, et al. ∙

research

∙ 06/18/2020

Interactive Recommender System via Knowledge Graph-enhanced Reinforcement Learning

Interactive recommender system (IRS) has drawn huge attention because of...

0 Sijin Zhou, et al. ∙

research

∙ 04/25/2020

Inter-sequence Enhanced Framework for Personalized Sequential Recommendation

Modeling the sequential correlation of users' historical interactions is...

0 Feng Liu, et al. ∙

research

∙ 04/14/2020

Personalized Re-ranking for Improving Diversity in Live Recommender Systems

Users of industrial recommender systems are normally suggesteda list of ...

0 Yichao Wang, et al. ∙

research

∙ 03/25/2020

AutoFIS: Automatic Feature Interaction Selection in Factorization Models for Click-Through Rate Prediction

Learning effective feature interactions is crucial for click-through rat...

2 Bin Liu, et al. ∙

research

∙ 01/22/2020

MetaSelector: Meta-Learning for Recommendation with User-Level Adaptive Model Selection

Recommender systems often face heterogeneous datasets containing highly ...

0 Mi Luo, et al. ∙

research

∙ 01/01/2020

Multi-Graph Convolution Collaborative Filtering

Personalized recommendation is ubiquitous, playing an important role in ...

0 Jianing Sun, et al. ∙

research

∙ 12/03/2019

Less Is Better: Unweighted Data Subsampling via Influence Function

In the time of Big Data, training complex models on large-scale data set...

20 Zifeng Wang, et al. ∙

research

∙ 05/24/2019

Learning Cross-Domain Representation with Multi-Graph Neural Network

Learning effective embedding has been proved to be useful in many real-w...

0 Yi Ouyang, et al. ∙

research

∙ 07/01/2018

Product-based Neural Networks for User Response Prediction over Multi-field Categorical Data

User response prediction is a crucial component for personalized informa...

0 Yanru Qu, et al. ∙

research

∙ 04/12/2018

DeepFM: An End-to-End Wide & Deep Learning Framework for CTR Prediction

Learning sophisticated feature interactions behind user behaviors is cri...

0 Huifeng Guo, et al. ∙

research

∙ 03/09/2018

Collaborative Filtering with Graph-based Implicit Feedback

Introducing consumed items as users' implicit feedback in matrix factori...

0 Minzhe Niu, et al. ∙

research

∙ 02/23/2018

Novel Approaches to Accelerating the Convergence Rate of Markov Decision Process for Search Result Diversification

Recently, some studies have utilized the Markov Decision Process for div...

0 Feng Liu, et al. ∙

research

∙ 02/22/2018

Federated Meta-Learning for Recommendation

Recommender systems have been widely studied from the machine learning p...

0 Fei Chen, et al. ∙

research

∙ 03/13/2017

DeepFM: A Factorization-Machine based Neural Network for CTR Prediction

Learning sophisticated feature interactions behind user behaviors is cri...

0 Huifeng Guo, et al. ∙

Xiuqiang He

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