#Paper Reading# Efficient Heterogeneous Collaborative Filtering without NS for Recommendation

论文题目: Efficient Heterogeneous Collaborative Filtering without Negative Sampling for Recommendation
论文地址: https://ojs.aaai.org/index.php/AAAI/article/view/5329
论文发表于: AAAI 2020（CCF A类会议）
论文所属单位: 清华大学

论文大体内容：
本文主要提出了EHCF（Efficient Heterogeneous Collaborative Filtering）算法，用于解决推荐中的问题。

Motivation：
传统的做法一般会进行负采样来做，但是负采样存在不足之处（样本分布偏差），同时没有考虑到用户行为类型（label）之间存在递进的关系性；

Contribution：
①提出了时间复杂度可控的算法，应用正样本数据来解决问题；
②提出了EHCF算法，通过传递各种行为的预测，来捕捉行为间的复杂关系；
③实验证明有效；

1. EHCF的框架，主要思考点是各个label之间的转移矩阵；

2. Loss可以化简为仅使用正样本数据去计算，从而降低算法复杂度；

实验
3. Dataset

4. Baseline

5. 实验结果

稀疏label下EHCF的作用更大

参考资料：
[1] https://github.com/chenchongthu/EHCF

以上均为个人见解，因本人水平有限，如发现有所错漏，敬请指出，谢谢！

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