STAMP: Short-Term Attention:Memory Priority Model for Session-based Recommendation簡介
STAMP: Short-Term Attention/Memory Priority Model for Session-based Recommendation 介紹 作者提出lstm雖然能很好的捕捉用戶的總體的興趣，但是作者指出lstm也許對於建模長的session是不夠有效的，因此作者提出STAMP，不僅捕捉用戶整體的興趣同時也將用戶當前的興趣納入考慮。 方法 STMP 文章首先介紹了ST
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相關文章
- 1. STAMP: Short-Term Attention:Memory Priority Model for Session-based Recommendation簡介
- 2. 【文獻閱讀】 2018-SIGKDD-STAMP:Short-Term Attention/Memory Priority Model for Session-based Recommendation
- 3. NAIS: Neural Attentive Item Similarity Model for Recommendation簡析
- 4. Deep Learning Recommendation Model for Personalization and Recommendation Systems
- 5. Generative Adversarial User Model for Reinforcement Learning Based Recommendation System
- 6. A Dynamic Recurrent Model for Next Basket Recommendation
- 7. #Paper Reading# Deep Learning Recommendation Model for Personalization and Recommendation Systems
- 8. Feature Re-Learning with Data Augmentation for Content-based Video Recommendation簡介
- 9. GNN:Session-based Recommendation with Graph Neural Networks簡介
- 10. Next Item Recommendation with Self-Attention簡介