NAIS: Neural Attentive Item Similarity Model for Recommendation簡析
NAIS: Neural Attentive Item Similarity Model for Recommendation PRELIMINARIES Standard Item-based CF 爲了預測用戶u對於物品i的評分，ItemCF的最基本思想是計算物品i與用戶u以前交互過的全部物品的類似性，預測評分計算公式以下：html R u + R_u^+ Ru+是用戶交互過的全部物品， s
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相關文章
- 1. 《NAIS: Neural Attentive Item Similarity Model for Recommendation》論文閱讀及解析
- 2. 論文筆記：NAIS: Neural Attentive Item Similarity Model for Recommendation
- 3. 推薦系統遇上深度學習(三十三)--Neural Attentive Item Similarity Model
- 4. NARM:Neural Attentive Session-based Recommendation簡析
- 5. Graph Neural Networks for Social Recommendation
- 6. Controllable Multi-Interest Framework for Recommendation
- 7. GNN:Session-based Recommendation with Graph Neural Networks簡介
- 8. Metapath-guided Heterogeneous Graph Neural Network for Intent Recommendation
- 9. Deep Learning Recommendation Model for Personalization and Recommendation Systems
- 10. Relational Collaborative Filtering: Modeling Multiple Item Relations for Recommendation簡析