筆記：《Transparent, Scrutable and Explainable User Models for Personalized Recommendation》
閱讀日期：2020.3.5 論文題目：《Transparent, Scrutable and Explainable User Models for Personalized Recommendation》 發現問題： 現有推薦系統：①透明度：由於算法過於複雜，或系統提供者進行了保護措施，使得現有推薦系統的透明度不高，僅提供一些justification（可能與推薦算法不相關的描述）。②可檢查性：
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相關文章
- 1. Non-Sampling Learning for Personalized Recommendation——筆記
- 2. 論文筆記：Personalized Deep Learning for Tag Recommendation
- 3. Real-Time Personalized Recommendation System
- 4. Explainable Recommendation: A Survey and New Perspectives讀書筆記
- 5. Explainable Reasoning over Knowledge Graphs for Recommendation
- 6. 【論文閱讀】 2014SIGIR - Explicit Factor Models for Explainable Recommendation based on Phrase-level Senti
- 7. 論文Reinforcement Knowledge Graph Reasoning for Explainable Recommendation筆記
- 8. Tree-enhanced Embedding Model for Explainable Recommendation閱讀筆記
- 9. 《Mining Large Streams of User Data for Personalized Recommendations》筆記
- 10. 論文筆記：Personalized Tag Recommendation for Images Using Deep Transfer Learning