论文解读：MeLU:Meta-Learned User Preference Estimator for Cold-Start Recommendation
论文解读：MeLU:Meta-Learned User Preference Estimator for Cold-Start Recommendation 推荐领域内的一个痛点问题是冷启动问题，现如今许多学术界在推荐领域内的研究都指向该问题。事实上，冷启动可以认为是一种由于用户-物品交互数据缺乏所导致的，自然而然可以想到利用小样本学习的方法，使得我们训练的模型可以在很少的样本前提下就能够给出
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相关文章
- 1. 《MeLU：Meta-Learned User Preference Estimator for Cold-Start Recommendation 》论文走读
- 2. 论文Meta-Learning for User Cold-Start Recommendation走读
- 3. 【论文理解】Graph Neural News Recommendation with Unsupervised Preference Disentanglement
- 4. 【论文解读 KDD 2019 | MEIRec】Metapath-guided Heterogeneous Graph Neural Network for Intent Recommendation
- 5. 《User Diverse Preference Modeling by Multimodal Attentive Metric Learning》论文阅读
- 6. 『 论文阅读』A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems
- 7. 推荐系统论文阅读：User Profiling based Deep Neural Network for Temporal News Recommendation
- 8. 《NAIS: Neural Attentive Item Similarity Model for Recommendation》论文阅读及解析
- 9. Learning over Knowledge-Base Embeddings for Recommendation 论文
- 10. 论文阅读——Predictor-Estimator using Multilevel Task Learning with Stack Propagation for Neural QE