Wide & Deep Learning for Recommender Systems论文笔记
Wide & Deep Learning for Recommender Systems论文笔记 摘要 具有非线性特征变换的广义线性模型广泛应用于具有稀疏输入的大规模回归和分类问题。通过大量的跨产品特征转换来记忆特征交互是有效的和可解释的，而泛化则需要更多的特征工程工作。在特征工程较少的情况下，通过对稀疏特征的低维密集嵌入学习，深层神经网络可以更好地推广到不可见的特征组合。然而，当用户-项目交互稀
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相关文章
- 1. 论文笔记 - Wide & Deep Learning for Recommender Systems
- 2. 论文笔记：Wide & Deep Learning for Recommender Systems
- 3. 《Wide & Deep Learning for Recommender Systems》论文总结
- 4. 论文阅读：《Wide & Deep Learning for Recommender Systems》
- 5. Wide & Deep Learning for Recommender Systems
- 6. Wide &Deep Learning for Recommender Systems
- 7. #Paper Reading# Wide & Deep Learning for Recommender Systems
- 8. Wide & Deep Learning for Recommender Systems 翻译
- 9. Wide & Deep Learning for Recommender Systems 模型实践
- 10. 《Wide and deep learning in Recommender Systems》论文阅读笔记