Learning Tree-based DeepModel for Recommender Systems
摘要 已经研究了用于推荐系统的基于模型的方法以提供更精确的结果。在具有 大型语料库的系统中，预测所有用户 - 项目对的偏好的学习模型的计算量是巨大的，这使得该模型难以直接用于候选推荐生成阶段。为了克服计算障碍，像矩阵分解这样的模型可以采用内积形式（即用用户和项目的潜在因素的内积作为偏好）和像哈希这样的索引执行有效的近似k-最近邻搜索。但是，其他由于计算量大，用户和项目特征之间的 表达性交互形式（例
相关文章
- 1. Wide & Deep Learning for Recommender Systems
- 2. Paper Reading:Wide & Deep Learning for Recommender Systems
- 3. Collaborative Deep Learning for Recommender Systems
- 4. Wide &Deep Learning for Recommender Systems
- 5. machine learning 之 Recommender Systems
- 6. Wide & Deep Learning for Recommender Systems论文笔记
- 7. 《Wide & Deep Learning for Recommender Systems》论文总结
- 8. Deep Learning Architecture for Collaborative Filtering Recommender Systems
- 9. 论文笔记 - Wide & Deep Learning for Recommender Systems
- 10. #Paper Reading# Wide & Deep Learning for Recommender Systems
- 更多相关文章...
- • Swift for 循环 - Swift 教程
- • Scala for循环 - Scala教程
- • Java Agent入门实战（三）-JVM Attach原理与使用
- • Java Agent入门实战（一）-Instrumentation介绍与使用
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. Wide & Deep Learning for Recommender Systems
- 2. Paper Reading:Wide & Deep Learning for Recommender Systems
- 3. Collaborative Deep Learning for Recommender Systems
- 4. Wide &Deep Learning for Recommender Systems
- 5. machine learning 之 Recommender Systems
- 6. Wide & Deep Learning for Recommender Systems论文笔记
- 7. 《Wide & Deep Learning for Recommender Systems》论文总结
- 8. Deep Learning Architecture for Collaborative Filtering Recommender Systems
- 9. 论文笔记 - Wide & Deep Learning for Recommender Systems
- 10. #Paper Reading# Wide & Deep Learning for Recommender Systems