arXiv (All)
open access
Distance-aware Self-adaptive Graph Convolution for Fine-grained Hierarchical Recommendation
Graph Convolutional Networks (GCNs) are widely used to improve recommendation accuracy and performance by effectively learning the representations of user and item nodes. However, two major challenges remain: (1) the lack of further optimization in the graph representation structure and (2) insuffic
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Graph Foundation Models for Recommendation: A Comprehensive Survey#971679
- Federated recommender systems based on deep learning: The experimental comparisons of deep learning algorithms and federated learning aggregation strategies.#489648
- GPU-Native Approximate Nearest Neighbor Search with IVF-RaBitQ: Fast Index Build and Search#785115
- GPU-Native Approximate Nearest Neighbor Search with IVF-RaBitQ: Fast Index Build and Search#783525
- Understanding and Mitigating Under-Confidence in GNNs from the Final Layer#1117228
Record · ID 1129077
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive