research
∙
08/15/2023
Dynamic Embedding Size Search with Minimum Regret for Streaming Recommender System
With the continuous increase of users and items, conventional recommende...
share
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 ...
research
∙
03/21/2023
Dynamically Expandable Graph Convolution for Streaming Recommendation
Personalized recommender systems have been widely studied and deployed t...
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...
research
∙
02/06/2023
Spectral Augmentations for Graph Contrastive Learning
Contrastive learning has emerged as a premier method for learning repres...
research
∙
01/07/2023
Bidirectional Learning for Offline Model-based Biological Sequence Design
Offline model-based optimization aims to maximize a black-box objective ...
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 ...
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...
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...
research
∙
11/11/2022
Intent-aware Multi-source Contrastive Alignment for Tag-enhanced Recommendation
To offer accurate and diverse recommendation services, recent methods us...
research
∙
10/30/2022
DyG2Vec: Representation Learning for Dynamic Graphs with Self-Supervision
The challenge in learning from dynamic graphs for predictive tasks lies ...
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...
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(...
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...
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...
research
∙
08/02/2022
Adapting Triplet Importance of Implicit Feedback for Personalized Recommendation
Implicit feedback is frequently used for developing personalized recomme...
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...
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...
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...
research
∙
11/10/2021
Generalizable Cross-Graph Embedding for GNN-based Congestion Prediction
Presently with technology node scaling, an accurate prediction model at ...
research
∙
10/25/2021
Content Filtering Enriched GNN Framework for News Recommendation
Learning accurate users and news representations is critical for news re...
research
∙
10/11/2021
WeTS: A Benchmark for Translation Suggestion
Translation Suggestion (TS), which provides alternatives for specific wo...
research
∙
08/14/2021
Modeling Scale-free Graphs for Knowledge-aware Recommendation
Aiming to alleviate data sparsity and cold-start problems of traditional...
research
∙
06/10/2021
RNN with Particle Flow for Probabilistic Spatio-temporal Forecasting
Spatio-temporal forecasting has numerous applications in analyzing wirel...
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 ...
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...
research
∙
01/13/2021
Knowledge-Enhanced Top-K Recommendation in Poincaré Ball
Personalized recommender systems are increasingly important as more cont...
research
∙
01/13/2021
Probabilistic Metric Learning with Adaptive Margin for Top-K Recommendation
Personalized recommender systems are playing an increasingly important r...
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...
research
∙
08/25/2020
GraphSAIL: Graph Structure Aware Incremental Learning for Recommender Systems
Given the convenience of collecting information through online services,...
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...
research
∙
06/23/2020
Non-Parametric Graph Learning for Bayesian Graph Neural Networks
Graphs are ubiquitous in modelling relational structures. Recent endeavo...
research
∙
01/01/2020
Multi-Graph Convolution Collaborative Filtering
Personalized recommendation is ubiquitous, playing an important role in ...
research
∙
12/26/2019
Memory Augmented Graph Neural Networks for Sequential Recommendation
The chronological order of user-item interactions can reveal time-evolvi...
research
∙
10/21/2019
Semantic Graph Convolutional Network for Implicit Discourse Relation Classification
Implicit discourse relation classification is of great importance for di...
research
∙
11/28/2018
A Graph-CNN for 3D Point Cloud Classification
Graph convolutional neural networks (Graph-CNNs) extend traditional CNNs...
research
∙
11/27/2018
Bayesian graph convolutional neural networks for semi-supervised classification
Recently, techniques for applying convolutional neural networks to graph...
research
∙
09/27/2018