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Found 6 papers, 2 papers with code
A Dynamic Linear Bias Incorporation Scheme for Nonnegative Latent Factor Analysis
Nonnegative Latent Factor Analysis (NLFA) models have proven to possess the superiority to address this issue, where a linear bias incorporation (LBI) scheme is important in present the training overshooting and fluctuation, as well as preventing the model from premature convergence.
Proximal Symmetric Non-negative Latent Factor Analysis: A Novel Approach to Highly-Accurate Representation of Undirected Weighted Networks
An Undirected Weighted Network (UWN) is commonly found in big data-related applications.
Multi-constrained Symmetric Nonnegative Latent Factor Analysis for Accurately Representing Large-scale Undirected Weighted Networks
An Undirected Weighted Network (UWN) is frequently encountered in a big-data-related application concerning the complex interactions among numerous nodes, e. g., a protein interaction network from a bioinformatics application.
Position-Aware Subgraph Neural Networks with Data-Efficient Learning
2) Prevailing graph augmentation methods for GEL, including rule-based, sample-based, adaptive, and automated methods, are not suitable for augmenting subgraphs because a subgraph contains fewer nodes but richer information such as position, neighbor, and structure.
An Unconstrained Symmetric Nonnegative Latent Factor Analysis for Large-scale Undirected Weighted Networks
It can naturally be quantified as a symmetric high-dimensional and incomplete (SHDI) matrix for implementing big data analysis tasks.
Adversarial and Contrastive Variational Autoencoder for Sequential Recommendation
To solve the above problem, in this work, we propose a novel method called Adversarial and Contrastive Variational Autoencoder (ACVAE) for sequential recommendation.
