MetaSelector: Meta-Learning for Recommendation with User-Level Adaptive Model Selection 走读
摘要 推荐系统经常面对包含高度个性化的用户历史数据的异构数据集，在这些数据集中，没有哪个模型可以为每个用户提供最佳建议。我们在公共和私有数据集上都观察到了这种普遍存在的现象，并在追求优化每个用户的推荐质量的过程中解决了模型选择问题。我们提出了一个元学习框架，以促进推荐系统中对用户类型的自适应模型选择。在此框架中，将使用来自所有用户的数据来训练推荐系统集合，然后通过元学习对模型选择器进行训练，以使用
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相关文章
- 1. AdaptIS: Adaptive Instance Selection Network
- 2. Deep Learning Recommendation Model for Personalization and Recommendation Systems
- 3. 【文献阅读】 2018-SIGKDD-STAMP:Short-Term Attention/Memory Priority Model for Session-based Recommendation
- 4. Tree-enhanced Embedding Model for Explainable Recommendation阅读笔记
- 5. NAIS: Neural Attentive Item Similarity Model for Recommendation简析
- 6. Generative Adversarial User Model for Reinforcement Learning Based Recommendation System
- 7. A Dynamic Recurrent Model for Next Basket Recommendation
- 8. CVPR2020--ATSS： Adaptive Training Sample Selection
- 9. Contextualized Graph Attention Network for Recommendation with Item Knowledge Graph
- 10. 《NAIS: Neural Attentive Item Similarity Model for Recommendation》论文阅读及解析