《MeLU：Meta-Learned User Preference Estimator for Cold-Start Recommendation 》論文走讀
摘要 本文提出了一種新的推薦系統，解決了基於少量樣本物品來估計用戶偏好的冷啓動問題。爲了確定用戶在冷啓動狀態下的偏好，現有的推薦系統，如Netflix，最初向用戶提供商品，我們稱這些物品爲候選商品。然後根據用戶選擇的物品提出建議。以往的推薦研究有兩個侷限性:(1)消費了少量商品的用戶推薦不佳，(2)候選的商品過少或者不準備，不能夠較好的反應用戶的偏好。爲了克服這兩個限制，我們提出了一種基於元學習的
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相關文章
- 1. 論文解讀：MeLU:Meta-Learned User Preference Estimator for Cold-Start Recommendation
- 2. 論文Meta-Learning for User Cold-Start Recommendation走讀
- 3. MetaSelector: Meta-Learning for Recommendation with User-Level Adaptive Model Selection 走讀
- 4. 【論文理解】Graph Neural News Recommendation with Unsupervised Preference Disentanglement
- 5. 《User Diverse Preference Modeling by Multimodal Attentive Metric Learning》論文閱讀
- 6. 『 論文閱讀』A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems
- 7. 推薦系統論文閱讀：User Profiling based Deep Neural Network for Temporal News Recommendation
- 8. Learning over Knowledge-Base Embeddings for Recommendation 論文
- 9. 論文閱讀——Predictor-Estimator using Multilevel Task Learning with Stack Propagation for Neural QE
- 10. RKGE Recurrent Knowledge Graph Embedding for Effective Recommendation 論文