A pytorch implementation of CFGAN
paper title: CFGAN:A Generic Collaborative Filtering Framework based on Generative Adversarial Networks
python CFGAN.py
A pytorch implementation of CFGAN
你好,我想请教一下关于数据集划分的问题,关于数据集划分,论文中提到了数据集随机划分为80%训练集,20%测试集,而我用了两种方法来划分:一种是整体数据集上直接随机80%为训练集,20%为测试集,另外一种是对于每一个用户交互过的项目的80%作为训练集,20%作为测试集。但是通过这两种方法划分出来的数据集都没有达到论文中的最优结果,我想问一下,是我划分数据集的方式不对,还是说我其他方面出现了问题。
作者您好,我觉得在G的部分关于结果的PM和ZR部分存在问题,PM应该保证mask的item都是unlabeled数据,也就是没有反馈信息的item,ZR应该保证1和0都是准确的取样,你的代码都是随机的可能效果不佳
May I ask how the generator obtains the data features?
你好,请问现在代码复现完成了吗
你好,我认为你的recall指标计算有误,recall的计算公式如下:
$$
recall = \frac{\sum_{u\in U}|R(u)\cap T(u)|}{\sum_{u\in U}|T(u)|}
$$
而你代码的实现却是如下公式:
$$
recall = \frac{\sum_{u\in U}\frac{|R(u)\cap T(u)|}{|T(u)|}}{|U|}
$$
如果我的理解有误,那请不吝赐教😃
请问0.45 的 precision 是怎么训练的到的,我用了您代码的默认设置得到的结果只有0.08,调参后也无法复现您所提到的结果。
麻烦您解答一下,谢谢。
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