Ruiming Tang

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HUAWEI Technologies Co., Ltd. ∙ National University of Singapore ∙ Harbin Institute of Technology

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

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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

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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

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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

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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

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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

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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

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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

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Multi-Graph Convolution Collaborative Filtering

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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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Ruiming Tang

Featured Co-authors

Jun Wang

Weinan Zhang

Yong Yu

Bin Liu

Feng Liu

Xiangyu Zhang

Qing Liu

Zhenguo Li

Wei Guo

Xiuqiang He

Han Cai

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