PyTorch implementation of the paper.
Modeling Delayed Feedback in Display Advertising, Olivier Chapelle, KDD2014
columns
feature1...feature_n: Categorical feautre column. (Assuming all variables are categorical.)elapsed_day: Days elapsed since click.cv_delay_day: Days delayed from click to conversion. (Only observable if conversions are observed)supervised: Conversion label. (If conversions are observed 1)
sample dataset
feature1 feature2 feature3 elapsed_day cv_delay_day supervised
0 1 1 1 10 3.0 1
1 3 3 3 3 NaN 0
2 5 5 5 30 NaN 0
3 7 7 7 2 1.0 1
4 2 2 2 6 NaN 0
5 5 5 5 1 NaN 0
6 1 1 1 11 8.0 1
7 3 3 3 32 NaN 0
In the paper, feature hashing is used for vectorization of categorical variables.
All the features are mapped into a sparse binary feature vector of dimension 2^24 via the hashing trick [17].
In this implementation, embedding layer is used instead of feature hashing for vectorization.