WebJan 1, 2024 · The proposed model is easy to implement and efficient to optimize and is shown to outperform state-of-the-art top-N recommendation methods that use side information. Read more Preprint WebTo address the above issues, we propose interpretable and efficient Heterogeneous Graph Convolutional Network (ie-HGCN) to learn representations of nodes in HINs. It automatically extracts useful meta-paths for each node from all possible meta-paths (within a length limit determined by the model depth), which brings good model interpretability.

Transfer Learning Across Heterogeneous Features For Efficient …

WebTo address the above issues, we propose an interpretable and efficient Heterogeneous Graph Convolutional Network (ie-HGCN) to learn the representations of objects in HINs. … WebApr 14, 2024 · In this study, we introduce an interpretable graph-based deep learning prediction model, AttentionSiteDTI, which utilizes protein binding sites along with a self-attention mechanism to address the ... perkins tryon schools calendar

Interpretable machine learning for knowledge generation in

WebBy making good use of item’s attribute, the networks will gain better interpretability. In this article, we construct a heterogeneous tripartite graph consisting of user-item-feature, and propose the attention interaction graph convolutional neural network recommendation algorithm (ATGCN). WebJul 8, 2015 · In addition, the combination of matrix factorization and latent topics makes the recommendation result interpretable. Therefore, the above two issues are simultaneously solved. Through a real-world dataset, where user behaviors in three social media sites are collected, we demonstrate that the proposed model is effective in improving … WebFeb 14, 2024 · The proposed method organizes heterogeneous patent information as a knowledge graph, a graph-structured knowledge base that enables efficient integration and semantic interpretation of ... perkins tryon school jobs