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Found 8 papers, 2 papers with code
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.
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.
Blurring-Sharpening Process Models for Collaborative Filtering
Various methods have been proposed for collaborative filtering, ranging from matrix factorization to graph convolutional methods.
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Collaborative Filtering
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TimeKit: A Time-series Forecasting-based Upgrade Kit for Collaborative Filtering
We present a time-series forecasting-based upgrade kit (TimeKit), which works in the following way: it i) first decides a base collaborative filtering algorithm, ii) extracts user/item embedding vectors with the base algorithm from user-item interaction logs incrementally, e. g., every month, iii) trains our time-series forecasting model with the extracted time- series of embedding vectors, and then iv) forecasts the future embedding vectors and recommend with their dot-product scores owing to a recent breakthrough in processing complicated time- series data, i. e., neural controlled differential equations (NCDEs).
BasisVAE: Orthogonal Latent Space for Deep Disentangled Representation
In this paper, we propose a method to decompose the latent space into basis, and reconstruct it by linear combination of the latent bases.
Lifelog Patterns Analyzation using Graph Embedding based on Deep Neural Network
A lifelog is a kind of big data to analyze behavior patterns in the daily life of individuals collected from various smart de-vices.
Automatic Financial Trading Agent for Low-risk Portfolio Management using Deep Reinforcement Learning
The two primary goals of the portfolio management problem are maximizing profit and restrainting risk.
Learning Latent Semantic Representation from Pre-defined Generative Model
Unlike the conventional GAN models with hidden distribution of latent space, we define the distributions explicitly in advance that are trained to generate the data based on the corresponding features by inputting the latent variables that follow the distribution.
