MetaSelector: Meta-Learning for Recommendation with User-Level Adaptive Model Selection 走讀
摘要 推薦系統經常面對包含高度個性化的用戶歷史數據的異構數據集，在這些數據集中，沒有哪個模型可以爲每個用戶提供最佳建議。我們在公共和私有數據集上都觀察到了這種普遍存在的現象，並在追求優化每個用戶的推薦質量的過程中解決了模型選擇問題。我們提出了一個元學習框架，以促進推薦系統中對用戶類型的自適應模型選擇。在此框架中，將使用來自所有用戶的數據來訓練推薦系統集合，然後通過元學習對模型選擇器進行訓練，以使用
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相關文章
- 1. AdaptIS: Adaptive Instance Selection Network
- 2. Deep Learning Recommendation Model for Personalization and Recommendation Systems
- 3. 【文獻閱讀】 2018-SIGKDD-STAMP:Short-Term Attention/Memory Priority Model for Session-based Recommendation
- 4. Tree-enhanced Embedding Model for Explainable Recommendation閱讀筆記
- 5. NAIS: Neural Attentive Item Similarity Model for Recommendation簡析
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- 10. 《NAIS: Neural Attentive Item Similarity Model for Recommendation》論文閱讀及解析