b'Ruiming Tang'

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

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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

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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

Xiangyu Zhang

Bin Liu

Feng Liu

Bo Chen

Zhenguo Li

Qing Liu

Wei Guo

Xiuqiang He

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