NAIS: Neural Attentive Item Similarity Model for Recommendation简析
NAIS: Neural Attentive Item Similarity Model for Recommendation PRELIMINARIES Standard Item-based CF 为了预测用户u对于物品i的评分，ItemCF的最基本思想是计算物品i与用户u以前交互过的全部物品的类似性，预测评分计算公式以下：html R u + R_u^+ Ru+是用户交互过的全部物品， s
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相关文章
- 1. 《NAIS: Neural Attentive Item Similarity Model for Recommendation》论文阅读及解析
- 2. 论文笔记：NAIS: Neural Attentive Item Similarity Model for Recommendation
- 3. 推荐系统遇上深度学习(三十三)--Neural Attentive Item Similarity Model
- 4. NARM:Neural Attentive Session-based Recommendation简析
- 5. Graph Neural Networks for Social Recommendation
- 6. Controllable Multi-Interest Framework for Recommendation
- 7. GNN:Session-based Recommendation with Graph Neural Networks简介
- 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简析