b'Yingxue Zhang'

research

∙ 05/02/2023

Structure Aware Incremental Learning with Personalized Imitation Weights for Recommender Systems

Recommender systems now consume large-scale data and play a significant ...

0 Yuening Wang, et al. ∙

research

∙ 03/21/2023

Dynamically Expandable Graph Convolution for Streaming Recommendation

Personalized recommender systems have been widely studied and deployed t...

0 Bowei He, et al. ∙

research

∙ 02/22/2023

A Survey on User Behavior Modeling in Recommender Systems

User Behavior Modeling (UBM) plays a critical role in user interest lear...

0 Zhicheng He, et al. ∙

research

∙ 02/06/2023

Spectral Augmentations for Graph Contrastive Learning

Contrastive learning has emerged as a premier method for learning repres...

0 Amur Ghose, et al. ∙

research

∙ 01/07/2023

Bidirectional Learning for Offline Model-based Biological Sequence Design

Offline model-based optimization aims to maximize a black-box objective ...

0 Can Chen, et al. ∙

research

∙ 12/29/2022

Neighbor Auto-Grouping Graph Neural Networks for Handover Parameter Configuration in Cellular Network

The mobile communication enabled by cellular networks is the one of the ...

0 Mehrtash Mehrabi, et al. ∙

research

∙ 11/30/2022

Findings of the WMT 2022 Shared Task on Translation Suggestion

We report the result of the first edition of the WMT shared task on Tran...

0 Zhen Yang, et al. ∙

research

∙ 11/17/2022

Sub-Graph Learning for Spatiotemporal Forecasting via Knowledge Distillation

One of the challenges in studying the interactions in large graphs is to...

0 Mehrtash Mehrabi, et al. ∙

research

∙ 11/11/2022

Intent-aware Multi-source Contrastive Alignment for Tag-enhanced Recommendation

To offer accurate and diverse recommendation services, recent methods us...

0 Haolun Wu, et al. ∙

research

∙ 10/30/2022

DyG2Vec: Representation Learning for Dynamic Graphs with Self-Supervision

The challenge in learning from dynamic graphs for predictive tasks lies ...

0 Mohammad Ali Alomrani, et al. ∙

research

∙ 09/15/2022

Bidirectional Learning for Offline Infinite-width Model-based Optimization

In offline model-based optimization, we strive to maximize a black-box o...

0 Can Chen, 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(...

0 Fuyuan Lyu, et al. ∙

research

∙ 08/04/2022

Node Copying: A Random Graph Model for Effective Graph Sampling

There has been an increased interest in applying machine learning techni...

0 Florence Regol, 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...

0 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...

0 Haolun Wu, 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...

0 Yankai Chen, et al. ∙

research

∙ 12/23/2021

Physics Constrained Flow Neural Network for Short-Timescale Predictions in Data Communications Networks

Machine learning is gaining growing momentum in various recent models fo...

0 Xiangle Cheng, 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

∙ 11/10/2021

Generalizable Cross-Graph Embedding for GNN-based Congestion Prediction

Presently with technology node scaling, an accurate prediction model at ...

0 Amur Ghose, 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

∙ 10/11/2021

WeTS: A Benchmark for Translation Suggestion

Translation Suggestion (TS), which provides alternatives for specific wo...

0 Zhen Yang, et al. ∙

research

∙ 08/14/2021

Modeling Scale-free Graphs for Knowledge-aware Recommendation

Aiming to alleviate data sparsity and cold-start problems of traditional...

0 Yankai Chen, et al. ∙

research

∙ 06/10/2021

RNN with Particle Flow for Probabilistic Spatio-temporal Forecasting

Spatio-temporal forecasting has numerous applications in analyzing wirel...

0 Soumyasundar Pal, 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

∙ 04/17/2021

TIE: A Framework for Embedding-based Incremental Temporal Knowledge Graph Completion

Reasoning in a temporal knowledge graph (TKG) is a critical task for inf...

0 Jiapeng Wu, et al. ∙

research

∙ 01/13/2021

Knowledge-Enhanced Top-K Recommendation in Poincaré Ball

Personalized recommender systems are increasingly important as more cont...

0 Chen Ma, 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...

0 Chen Ma, et al. ∙

research

∙ 10/10/2020

MS-Ranker: Accumulating Evidence from Potentially Correct Candidates for Answer Selection

As conventional answer selection (AS) methods generally match the questi...

0 Yingxue Zhang, et al. ∙

research

∙ 08/25/2020

GraphSAIL: Graph Structure Aware Incremental Learning for Recommender Systems

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

39 Yishi Xu, et al. ∙

research

∙ 07/09/2020

Active Learning on Attributed Graphs via Graph Cognizant Logistic Regression and Preemptive Query Generation

Node classification in attributed graphs is an important task in multipl...

0 Florence Regol, et al. ∙

research

∙ 06/23/2020

Non-Parametric Graph Learning for Bayesian Graph Neural Networks

Graphs are ubiquitous in modelling relational structures. Recent endeavo...

0 Soumyasundar Pal, 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

∙ 12/26/2019

Memory Augmented Graph Neural Networks for Sequential Recommendation

The chronological order of user-item interactions can reveal time-evolvi...

0 Chen Ma, et al. ∙

research

∙ 10/21/2019

Semantic Graph Convolutional Network for Implicit Discourse Relation Classification

Implicit discourse relation classification is of great importance for di...

0 Yingxue Zhang, et al. ∙

research

∙ 11/28/2018

A Graph-CNN for 3D Point Cloud Classification

Graph convolutional neural networks (Graph-CNNs) extend traditional CNNs...

0 Yingxue Zhang, et al. ∙

research

∙ 11/27/2018

Bayesian graph convolutional neural networks for semi-supervised classification

Recently, techniques for applying convolutional neural networks to graph...

0 Yingxue Zhang, et al. ∙

research

∙ 09/27/2018

Point-of-Interest Recommendation: Exploiting Self-Attentive Autoencoders with Neighbor-Aware Influence

The rapid growth of Location-based Social Networks (LBSNs) provides a gr...

0 Chen Ma, et al. ∙

Yingxue Zhang

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