b'Ruiming Tang'

research

∙ 08/11/2022

Task Aligned Meta-learning based Augmented Graph for Cold-Start Recommendation

The cold-start problem is a long-standing challenge in recommender syste...

Yuxiang Shi, et al. ∙

research

∙ 08/09/2022

OptEmbed: Learning Optimal Embedding Table for Click-through Rate Prediction

Learning embedding table plays a fundamental role in Click-through rate(...

Fuyuan Lyu, et al. ∙

research

∙ 08/03/2022

Coarse-to-Fine Knowledge-Enhanced Multi-Interest Learning Framework for Multi-Behavior Recommendation

Multi-types of behaviors (e.g., clicking, adding to cart, purchasing, et...

Chang Meng, et al. ∙

research

∙ 08/02/2022

Adapting Triplet Importance of Implicit Feedback for Personalized Recommendation

Implicit feedback is frequently used for developing personalized recomme...

Haolun Wu, 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...

Jianghao Lin, et al. ∙

research

∙ 06/05/2022

Learning Binarized Graph Representations with Multi-faceted Quantization Reinforcement for Top-K Recommendation

Learning vectorized embeddings is at the core of various recommender sys...

Yankai Chen, et al. ∙

research

∙ 04/26/2022

Cross Pairwise Ranking for Unbiased Item Recommendation

Most recommender systems optimize the model on observed interaction data...

Qi Wan, et al. ∙

research

∙ 04/04/2022

Automated Machine Learning for Deep Recommender Systems: A Survey

Deep recommender systems (DRS) are critical for current commercial onlin...

Bo Chen, 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...

Yi Li, et al. ∙

research

∙ 01/27/2022

DropNAS: Grouped Operation Dropout for Differentiable Architecture Search

Neural architecture search (NAS) has shown encouraging results in automa...

Weijun Hong, 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...

Yankai Chen, et al. ∙

research

∙ 11/16/2021

On Effective Scheduling of Model-based Reinforcement Learning

Model-based reinforcement learning has attracted wide attention due to i...

Hang Lai, 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...

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...

Chenxu Zhu, 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...

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...

Yunjia Xi, et al. ∙

research

∙ 09/28/2021

Extracting Attentive Social Temporal Excitation for Sequential Recommendation

In collaborative filtering, it is an important way to make full use of s...

Yunzhe Li, 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...

Jiarui Qin, et al. ∙

research

∙ 06/25/2021

Balancing Accuracy and Fairness for Interactive Recommendation with Reinforcement Learning

Fairness in recommendation has attracted increasing attention due to bia...

Weiwen Liu, 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...

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 ...

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...

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,...

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...

Xinyi Dai, et al. ∙

research

∙ 01/13/2021

Probabilistic Metric Learning with Adaptive Margin for Top-K Recommendation

Personalized recommender systems are playing an increasingly important r...

Chen Ma, 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...

Huifeng Guo, 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...

Xinyi Dai, 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...

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 ...

Kelong Mao, et al. ∙

research

∙ 08/25/2020

GraphSAIL: Graph Structure Aware Incremental Learning for Recommender Systems

Given the convenience of collecting information through online services,...

Yishi Xu, 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...

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...

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 ...

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...

Bin Liu, et al. ∙

research

∙ 01/01/2020

Multi-Graph Convolution Collaborative Filtering

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

Jianing Sun, et al. ∙

research

∙ 04/09/2019

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

Click-Through Rate prediction is an important task in recommender system...

Bin Liu, et al. ∙

research

∙ 12/12/2018

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...

Qing Liu, et al. ∙

research

∙ 11/14/2018

Large-scale Interactive Recommendation with Tree-structured Policy Gradient

Reinforcement learning (RL) has recently been introduced to interactive ...

Haokun Chen, et al. ∙

research

∙ 10/29/2018

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

Recommendation is crucial in both academia and industry, and various tec...

Feng Liu, et al. ∙

research

∙ 07/16/2018

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

The KNN approach, which is widely used in recommender systems because of...

Huifeng Guo, 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...

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...

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...

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...

Feng Liu, 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...

Huifeng Guo, et al. ∙

Ruiming Tang

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