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Found 16 papers, 10 papers with code
QoS-Aware Graph Contrastive Learning for Web Service Recommendation
We propose a novel approach called QoS-aware graph contrastive learning (QAGCL) for web service recommendation.
RDGCL: Reaction-Diffusion Graph Contrastive Learning for Recommendation
Contrastive learning (CL) has emerged as a promising technique for improving recommender systems, addressing the challenge of data sparsity by leveraging self-supervised signals from raw data.
Learning Flexible Body Collision Dynamics with Hierarchical Contact Mesh Transformer
These methods are typically designed to i) reduce the computational cost in solving physical dynamics and/or ii) propose techniques to enhance the solution accuracy in fluid and rigid body dynamics.
An Attentive Inductive Bias for Sequential Recommendation beyond the Self-Attention
In the SR domain, we, for the first time, show that the same problem occurs.
Ranked #1 on
Sequential Recommendation
on MovieLens 1M
Polynomial-based Self-Attention for Table Representation learning
Structured data, which constitutes a significant portion of existing data types, has been a long-standing research topic in the field of machine learning.
Graph Convolutions Enrich the Self-Attention in Transformers!
Transformers, renowned for their self-attention mechanism, have achieved state-of-the-art performance across various tasks in natural language processing, computer vision, time-series modeling, etc.
Long-term Time Series Forecasting based on Decomposition and Neural Ordinary Differential Equations
Long-term time series forecasting (LTSF) is a challenging task that has been investigated in various domains such as finance investment, health care, traffic, and weather forecasting.
Graph Neural Rough Differential Equations for Traffic Forecasting
A prevalent approach in the field is to combine graph convolutional networks and recurrent neural networks for the spatio-temporal processing.
Ranked #2 on
Traffic Prediction
on PeMSD7(L)
GREAD: Graph Neural Reaction-Diffusion Networks
In particular, diffusion equations have been widely used for designing the core processing layer of GNNs, and therefore they are inevitably vulnerable to the notorious oversmoothing problem.
Time Series Forecasting with Hypernetworks Generating Parameters in Advance
Forecasting future outcomes from recent time series data is not easy, especially when the future data are different from the past (i. e. time series are under temporal drifts).
Blurring-Sharpening Process Models for Collaborative Filtering
Various methods have been proposed for collaborative filtering, ranging from matrix factorization to graph convolutional methods.
Ranked #1 on
Collaborative Filtering
on Gowalla
Prediction-based One-shot Dynamic Parking Pricing
Owing to the continuous and bijective characteristics of NODEs, in addition, we design a one-shot price optimization method given a pre-trained prediction model, which requires only one iteration to find the optimal solution.
Graph Neural Controlled Differential Equations for Traffic Forecasting
A prevalent approach in the field is to combine graph convolutional networks and recurrent neural networks for the spatio-temporal processing.
Ranked #3 on
Traffic Prediction
on PeMSD7(L)
Linear, or Non-Linear, That is the Question!
To our knowledge, we are the first who design a hybrid method and report the correlation between the graph centrality and the linearity/non-linearity of nodes.
Climate Modeling with Neural Diffusion Equations
On the other hand, neural ordinary differential equations (NODEs) are to learn a latent governing equation of ODE from data.
LT-OCF: Learnable-Time ODE-based Collaborative Filtering
In this work, we extend them based on neural ordinary differential equations (NODEs), because the linear GCN concept can be interpreted as a differential equation, and present the method of Learnable-Time ODE-based Collaborative Filtering (LT-OCF).
Ranked #1 on
Recommendation Systems
on Amazon-book
