Bayesian Personalized Ranking
在推薦系統的實現中，幾乎總會遇到從較多候選集中爲用戶選取特定的少數幾個物品進行推薦，這本質上是一個Ranking問題。 在推薦場景中用戶更缺乏耐性，對推薦結果的消費也十分有限。因此，排序的好壞直接決定了用戶對一個準確率爲90%的推薦候選集的滿意度是否真的有90%。 這裏我們爲大家介紹一種「基於貝葉斯後驗優化的個性化排序算法」：Bayesian Personalized Ranking。 其
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相關文章
- 1. VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback
- 2. Bayesian Personalized Ranking 算法解析及Python實現 似然與機率的異同
- 3. #Paper Reading# Personalized Context-aware Re-ranking for E-commerce Recommender Systems
- 4. Real-Time Personalized Recommendation System
- 5. Random Walk and Personalized Pagerank
- 6. 論文筆記：Personalized Deep Learning for Tag Recommendation
- 7. Personalized Defect Prediction
- 8. Naive Bayesian
- 9. Bayesian statistics
- 10. Bayesian Watchdog