CTR Algorithm
根据论文, 博客, 知乎等方式学习一些CTR相关的算法
理解原理并自己动手来实现一遍
pytorch & tf2.0
保持一颗学徒的心！
一些经典的CTR算法的复现; LR, FM, FFM, AFM, DeepFM，xDeepFM, PNN, DCN, DCNv2, DIFM, AutoInt, FiBiNet,AFN,ONN,DIN, DIEN ... （pytorch, tf2.0）
Overview
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Comments
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关于DIN的疑问
opened by yanduoduan 0大佬您好，最近看您的DIN代码，有一些地方不太明白，希望得到您的解答！ 1、mask = (behaviors_x > 0).float().unsqueeze(-1) 这里的msak的具体作用是啥啊？为什么需要这个mask呢？ 这里的 注意力输入部分，原始的好像没有queries - user_behavior吧？为啥有这一项呢 2、attn_input = torch.cat([queries, user_behavior, queries - user_behavior, queries * user_behavior], dim = -1) 3、 output = user_behavior.mul(attns.mul(mask)) # batch * seq_len * embed_dim 这个里面为啥还有mask呢？
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关于DIEN的PyTorch实现
opened by Q-Qing 1你好，我在阅读DIEN的PyTorch代码时发现一个地方可能存在错误 在Interest Extractor Layer中，GRU的隐藏层应该保留前T-1个用于计算辅助loss 但是DIEN代码的152行是
gru_embed=pad_interests[:,1:]这样是不是取了后T-1个hidden state? 我理解的应该是gru_embed=pad_interests[:,:-1] -
关于DIN模型的疑惑
opened by yanduoduan 4大佬您好，最近看您的DIN代码，有一些地方不太明白，希望得到您的解答！ 1、mask = (behaviors_x > 0).float().unsqueeze(-1) 这里的msak的具体作用是啥啊？为什么需要这个mask呢？ 这里的 注意力输入部分，原始的好像没有queries - user_behavior吧？为啥有这一项呢 2、attn_input = torch.cat([queries, user_behavior, queries - user_behavior, queries * user_behavior], dim = -1) 3、 output = user_behavior.mul(attns.mul(mask)) # batch * seq_len * embed_dim 这个里面为啥还有mask呢？
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模型中关于输入的feature_fields的疑惑
opened by yanduoduan 6大佬，您好，您写的这个项目是我看到最优美的推荐代码，对于小白的我，非常受益，感谢您把您的工作分享出来，供大家学习。在阅读代码的时候，我一直有一个小小的疑惑，就是，fields = data_x.max().values，这句话的意思是获取到每列特征中最大的索引，比如第一列是0，1两个不同的特征，而这句代码直接就取到了1，然而实际就有两个不同的特征，这样做embedding的时候，每一列的特征都少一个，所以torch.nn.Embedding(sum(feature_fields)+1,1)是不是应该为torch.nn.Embedding(sum(feature_fields+1),1)？还有为什么要在sum(feature_fields)后面加个1呢？还请大佬明示，感谢！
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