b'Ruiming Tang'

Balancing Accuracy and Fairness for Interactive Recommendation with Reinforcement Learning

Fairness in recommendation has attracted increasing attention due to bia...
06/25/2021 ∙ by Weiwen Liu, et al. ∙ 0 ∙ share

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

Recommender systems are often asked to serve multiple recommendation sce...
06/09/2021 ∙ by Xiangli Yang, et al. ∙ 0 ∙ share

read it
Dual Graph enhanced Embedding Neural Network for CTR Prediction

CTR prediction, which aims to estimate the probability that a user will ...
06/01/2021 ∙ by Wei Guo, et al. ∙ 0 ∙ share

read it
Deep Learning for Click-Through Rate Estimation

Click-through rate (CTR) estimation plays as a core function module in v...
04/21/2021 ∙ by Weinan Zhang, et al. ∙ 0 ∙ share

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

Because of the superior feature representation ability of deep learning,...
04/17/2021 ∙ by Huifeng Guo, et al. ∙ 0 ∙ share

read it
An Adversarial Imitation Click Model for Information Retrieval

Modern information retrieval systems, including web search, ads placemen...
04/13/2021 ∙ by Xinyi Dai, et al. ∙ 0 ∙ share

read it
Probabilistic Metric Learning with Adaptive Margin for Top-K Recommendation

Personalized recommender systems are playing an increasingly important r...
01/13/2021 ∙ by Chen Ma, et al. ∙ 0 ∙ share

read it
AutoDis: Automatic Discretization for Embedding Numerical Features in CTR Prediction

Learning sophisticated feature interactions is crucial for Click-Through...
12/16/2020 ∙ by Huifeng Guo, et al. ∙ 0 ∙ share

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

Learning to rank with implicit feedback is one of the most important tas...
11/01/2020 ∙ by Xinyi Dai, et al. ∙ 0 ∙ share

read it
A Practical Incremental Method to Train Deep CTR Models

Deep learning models in recommender systems are usually trained in the b...
09/04/2020 ∙ by Yichao Wang, et al. ∙ 0 ∙ share

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

Tagging has been recognized as a successful practice to boost relevance ...
08/26/2020 ∙ by Kelong Mao, et al. ∙ 0 ∙ share

read it
GraphSAIL: Graph Structure Aware Incremental Learning for Recommender Systems

Given the convenience of collecting information through online services,...
08/25/2020 ∙ by Yishi Xu, et al. ∙ 39 ∙ share

read it
Interactive Recommender System via Knowledge Graph-enhanced Reinforcement Learning

Interactive recommender system (IRS) has drawn huge attention because of...
06/18/2020 ∙ by Sijin Zhou, et al. ∙ 0 ∙ share

read it
Inter-sequence Enhanced Framework for Personalized Sequential Recommendation

Modeling the sequential correlation of users' historical interactions is...
04/25/2020 ∙ by Feng Liu, et al. ∙ 0 ∙ share

read it
Personalized Re-ranking for Improving Diversity in Live Recommender Systems

Users of industrial recommender systems are normally suggesteda list of ...
04/14/2020 ∙ by Yichao Wang, et al. ∙ 0 ∙ share

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

Learning effective feature interactions is crucial for click-through rat...
03/25/2020 ∙ by Bin Liu, et al. ∙ 2 ∙ share

read it
Multi-Graph Convolution Collaborative Filtering

Personalized recommendation is ubiquitous, playing an important role in ...
01/01/2020 ∙ by Jianing Sun, et al. ∙ 0 ∙ share

read it
Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction

Click-Through Rate prediction is an important task in recommender system...
04/09/2019 ∙ by Bin Liu, et al. ∙ 0 ∙ share

read it
An Efficient and Truthful Pricing Mechanism for Team Formation in Crowdsourcing Markets

In a crowdsourcing market, a requester is looking to form a team of work...
12/12/2018 ∙ by Qing Liu, et al. ∙ 0 ∙ share

read it
Large-scale Interactive Recommendation with Tree-structured Policy Gradient

Reinforcement learning (RL) has recently been introduced to interactive ...
11/14/2018 ∙ by Haokun Chen, et al. ∙ 0 ∙ share

read it
Deep Reinforcement Learning based Recommendation with Explicit User-Item Interactions Modeling

Recommendation is crucial in both academia and industry, and various tec...
10/29/2018 ∙ by Feng Liu, et al. ∙ 0 ∙ share

read it
An Adjustable Heat Conduction based KNN Approach for Session-based Recommendation

The KNN approach, which is widely used in recommender systems because of...
07/16/2018 ∙ by Huifeng Guo, et al. ∙ 0 ∙ share

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

User response prediction is a crucial component for personalized informa...
07/01/2018 ∙ by Yanru Qu, et al. ∙ 0 ∙ share

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

Learning sophisticated feature interactions behind user behaviors is cri...
04/12/2018 ∙ by Huifeng Guo, et al. ∙ 0 ∙ share

read it
Collaborative Filtering with Graph-based Implicit Feedback

Introducing consumed items as users' implicit feedback in matrix factori...
03/09/2018 ∙ by Minzhe Niu, et al. ∙ 0 ∙ share

read it
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...
02/23/2018 ∙ by Feng Liu, et al. ∙ 0 ∙ share

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

Learning sophisticated feature interactions behind user behaviors is cri...
03/13/2017 ∙ by Huifeng Guo, et al. ∙ 0 ∙ share

read it

Ruiming Tang

Featured Co-authors

Jun Wang

Pheng-Ann Heng

Weinan Zhang

Yong Yu

Xiangyu Zhang

Feng Liu

Bin Liu

Shuai Li

Bo Chen

Zhenguo Li

Jianye Hao

Balancing Accuracy and Fairness for Interactive Recommendation with Reinforcement Learning

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

Dual Graph enhanced Embedding Neural Network for CTR Prediction

Deep Learning for Click-Through Rate Estimation

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

An Adversarial Imitation Click Model for Information Retrieval

Probabilistic Metric Learning with Adaptive Margin for Top-K Recommendation

AutoDis: Automatic Discretization for Embedding Numerical Features in CTR Prediction

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

A Practical Incremental Method to Train Deep CTR Models

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

GraphSAIL: Graph Structure Aware Incremental Learning for Recommender Systems

Interactive Recommender System via Knowledge Graph-enhanced Reinforcement Learning

Inter-sequence Enhanced Framework for Personalized Sequential Recommendation

Personalized Re-ranking for Improving Diversity in Live Recommender Systems

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

Multi-Graph Convolution Collaborative Filtering

Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction

An Efficient and Truthful Pricing Mechanism for Team Formation in Crowdsourcing Markets

Large-scale Interactive Recommendation with Tree-structured Policy Gradient

Deep Reinforcement Learning based Recommendation with Explicit User-Item Interactions Modeling

An Adjustable Heat Conduction based KNN Approach for Session-based Recommendation

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

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

Collaborative Filtering with Graph-based Implicit Feedback

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

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