论文阅读：Collaborative Knowledge Base Embedding for Recommender Systems
1. Motivation 稀疏性问题是推荐系统必须要面临的一个挑战，把side-information包含进基于MF的方法是一个很好的方式。但是物品的属性是一种结构化的数据，用简单的MLP并不能学习到它们之间的关联，从而无法全面的刻画一个物品。本文提出基于知识图谱的embedding来刻画物品的显式信息，这种是从图结构中学到的关联，能够比较充分地学习到物品的表达。本文关于物品的表达有三个部分，另
相关文章
- 1. 论文阅读：Knowledge Graph Convolutional Networks for Recommender Systems
- 2. 论文阅读：《Wide & Deep Learning for Recommender Systems》
- 3. Collaborative Deep Learning for Recommender Systems
- 4. Knowledge Graph Convolutional Networks for Recommender Systems
- 5. Wide & Deep Learning for Recommender Systems 论文阅读总结
- 6. Side Information-Knowledge Graph Convolutional Networks for Recommender Systems阅读笔记
- 7. 论文阅读：Collaborative Deep Embedding Via Dual Networks
- 8. 【论文导读】MATRIX FACTORIZATION TECHNIQUES FOR RECOMMENDER SYSTEMS
- 9. 阅读笔记--RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems
- 10. Learning over Knowledge-Base Embeddings for Recommendation 论文
- 更多相关文章...
- • RSS 阅读器 - RSS 教程
- • PHP 实例 - AJAX RSS 阅读器 - PHP教程
- • JDK13 GA发布：5大特性解读
- • Scala 中文乱码解决
相关标签/搜索
每日一句
-
每一个你不满意的现在，都有一个你没有努力的曾经。
欢迎关注本站公众号,获取更多信息
相关文章
- 1. 论文阅读：Knowledge Graph Convolutional Networks for Recommender Systems
- 2. 论文阅读：《Wide & Deep Learning for Recommender Systems》
- 3. Collaborative Deep Learning for Recommender Systems
- 4. Knowledge Graph Convolutional Networks for Recommender Systems
- 5. Wide & Deep Learning for Recommender Systems 论文阅读总结
- 6. Side Information-Knowledge Graph Convolutional Networks for Recommender Systems阅读笔记
- 7. 论文阅读：Collaborative Deep Embedding Via Dual Networks
- 8. 【论文导读】MATRIX FACTORIZATION TECHNIQUES FOR RECOMMENDER SYSTEMS
- 9. 阅读笔记--RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems
- 10. Learning over Knowledge-Base Embeddings for Recommendation 论文