オンライン小売業者の格付けデータの違いを考慮したバンドル推奨方法
Yan Fang1, Qiuqin An1, Xue Jin1
1School of Maritime Economics and Management, Dalian Maritime University, Dalian, Liaoning, China.
PloS one
|September 3, 2025
まとめ
この研究では,電子商取引におけるユーザーの好みや満たされていない要求を理解するために,評価の格差を用いた新しい2段階のバンドル勧告の枠組みを導入しています. このモデルは,オンライン小売業者の推奨の正確性とユーザー満足度を大幅に改善します.
科学分野:
- 電子商取引
- マーケティング分析
- 推奨システム
背景:
- バンドリングは電子商取引の重要な戦略であり,小売業者と消費者の両方に利益をもたらします.
- 顧客の好みや満足度の理解には ユーザーによる製品評価が不可欠です
- データの希少性と異質性は,電子商取引の推奨システムに課題をもたらします.
研究 の 目的:
- 格付けの格差を活用する新しいバンドル勧告の枠組みを提案する.
- 評価の違いを分析することで,微妙なユーザーの好みや満たされていない要求を把握します.
- 電子商取引におけるバンドルの推奨の正確性とユーザー満足度を向上させる.
主な方法:
- データの希少性と異質性を扱う2段階の推奨方法
- ステージ" 格付けマトリックス完成のための共同フィルタリングによる深層単数値分解
- ステージ2 ユーザー不満をモデル化し,異質なデータを融合させるための二層グラフの自己注意ネットワーク
主要な成果:
- ノーマライズド・ディスコント・カミュレート・ゲイン (NDCG) とリコール・メトリックで3~6%の相対的な改善を達成した.
- 推奨されたバンドルのユーザー満足度の大幅な増加が示されました.
- 評価の差異を分析して 改善された勧告の有効性を検証した.
結論:
- 評価の格差は ユーザー行動や潜在的要求に 価値ある洞察を与えてくれます
- 提案された2段階のモデルは,バンドルの推奨のパフォーマンスを効果的に改善します.
- このフレームワークは,オンライン小売業者が顧客体験と販売を改善するための貴重なツールを提供します.
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