Why are categorical data all set 1?
See original GitHub issueIf do so,what`s the meaning of categorical data?
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因为这里有 numerical feature, 所以对于整个输入的 vector, 不能直接 tf.nn.embedding_lookup 就完事了, 因为对于 numerical feature 没法做到真正的 embedding. 因此, 这里的做法是, 拿到 tf.nn.embedding_lookup 之后, 和输入的 vector 直接相乘一下. 对于 categorical feature, 我们当然是希望保持 embedding 对应索引的那个 vector. 所以就直接置为 1 了, 这个主要是代码的 trick 吧.
please note this line(DeepFM.py, around line 88)
self.y_first_order = tf.reduce_sum(tf.multiply(self.y_first_order, feat_value), 2) # None * F
It’s not for categorical feature, it’s for numercal feature