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DRG:コースの推奨のための二重リレーショナルグラフフレームワーク

Yong Ouyang1, Zhen Ye1, Lingyu Chen1

  • 1College of Computer Science, Hubei University of Technology, Wuhan, 430068, PR China.

Neural networks : the official journal of the International Neural Network Society
|August 22, 2025
PubMed
まとめ
この要約は機械生成です。

この研究は,教育コースの推奨システムにおけるデータ不足と戦うために,二重関係グラフ (DRG) フレームワークを導入しています. DRGは二重関係をモデル化することで精度を高め,単一グラフのアプローチを上回ります.

キーワード:
コースの推奨コース関係グラフ二重関係グラフ大規模な言語モデル

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科学分野:

  • 教育技術
  • 人工知能
  • データサイエンス

背景:

  • コースの推奨システムは 個別化された学習と 教学の質の向上に不可欠です
  • 大型言語モデル (LLM) は有望ですが,データの希少性に悩んでいます.
  • データの希少性は,従来のLLMベースの推奨モデルの正確性を制限します.

研究 の 目的:

  • コースの推奨におけるデータ不足に対処するために,二重関係グラフ (DRG) フレームワークを提案する.
  • コース・コースとユーザー・コースの関係をモデル化して,推奨の正確性を向上させる.
  • 稀少な教育環境で個別化されたコースの推奨を可能にする 拡張可能で効果的なソリューションを開発する.

主な方法:

  • LLMの意味論推論,コラボレーションフィルタリング,クラスタリング,アソシエーションルールマイニングを使用してコースベースのグラフを構築する.
  • 共同フィルタリングとLLMの好み推論を介してユーザーベースのグラフを構築します.
  • 統合されたパイプライン内の共同学習と共同推論を通じて二重グラフを統合します.

主要な成果:

  • DRG フレームワークは2つのデータセットでリンクのカバー率を37.88%と12.67%増加させ,データの希少性を大幅に軽減しました.
  • DRGは,単一関係アプローチと比較して,タスクランキングで優れたパフォーマンスを示しました.
  • 提案されたDRGモジュールは,従来とLLMベースの推奨システムを強化しました.

結論:

  • 双重関係グラフ (DRG) フレームワークは,教育上の推奨システムにおけるデータ不足を効果的に解決します.
  • 双重関係をモデル化し,LLM主導の意味論理解を統合することで,推奨の精度が向上します.
  • DRGは,既存の推奨モデルを強化し,スケーラブルなソリューションを提供する,汎用性のあるプラグ&プレイモジュールです.