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View Code? Open in Web Editor NEW该仓库尝试整理推荐系统领域的一些经典算法模型
该仓库尝试整理推荐系统领域的一些经典算法模型
看了大佬用pytoych实现AFM,很佩服。本人最近在使用AFM,能给出一个借助deepctr_torch构建AFM的案例麽?万分感谢。
函数定义: def get_train_instances(train, num_negatives):
里面第二个for循环是不是应该整体往左缩进
大佬,有实现双塔召回的么
如W&D,Tensorflow版本中会建立sparse cols的1 dim embedding,并与dense cols 一起输入linear中,在pytorch版本中只有dense cols输入到linear中。
但是文章里面不是把特别稀疏的sparse cols输入到linear里面吗,为什么pytorch在linear里面就输入dense cols ?
dense_input, sparse_inputs = x[:, :len(elf.dense_feature_cols)], x[:, len(self.dense_feature_cols):]
sparse_inputs = sparse_inputs.long()
sparse_embeds = [self.embed_layers['embed_' + str(i)](sparse_inputs[:, i])
for i in range(sparse_inputs.shape[1])]
sparse_embeds = torch.cat(sparse_embeds, axis=-1)
dnn_input = torch.cat([sparse_embeds, dense_input], axis = -1)
wide_out = self.linear(dense_input)
deep_out = self.dnn_network(dnn_input)
deep_out = self.final_linear(deep_out)
outputs = F.Sigmoid(0.5*(wide_out + deep_out))
为什么PNN不用类似Deep Crossing中的resnet结构 而是普通的全连接层呢?
你好,请问可以在论文中引用这个新闻推荐数据集吗,需要引用什么吗?
UserCF中,若是val_data中的用户u若是不存在trn_data中,sim中就不存在用户u了,那如何给用户u做推荐?
相当于是一个新用户?做额外处理吗?
predictions = model(features)
print('shape: ', features.shape, predictions.shape, labels.shape)
shape: torch.Size([32, 39]) torch.Size([32, 1]) torch.Size([32])
labels的维度和预测结果维度不一致
我自己是做航空发动机寿命预测的,两个任务:寿命预测(回归),状态评估(分类);
我的训练集特征就是60秒内的健康指标的变化趋势,我不太明白如何将这个60*1的np.array转化为你的grad norm文件里面所写的张量。
即这句话我不太理解应该如何更改:train_model_input = {name: tf.keras.backend.constant(train_data[name]) for name in feature_names}
是否可以提供一下邮箱地址,想跟进一步询问一下大大
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