Weighted Multi-Level Feature Factorization for App ads CTR and
  installation prediction
Juan Manuel Rodriguez Antonela Tommasel
This paper provides an overview of the approach we used as team ISISTANITOS
for the ACM RecSys Challenge 2023. The competition was organized by ShareChat,
and involved predicting the probability of a user clicking an app ad and/or
installing an app, to improve deep funnel optimization and a special focus on
user privacy. Our proposed method inferring the probabilities of clicking and
installing as two different, but related tasks. Hence, the model engineers a
specific set of features for each task and a set of shared features. Our model
is called Weighted Multi-Level Feature Factorization because it considers the
interaction of different order features, where the order is associated to the
depth in a neural network. The prediction for a given task is generated by
combining the task specific and shared features on the different levels. Our
submission achieved the 11 rank and overall score of 55 in the competition
academia-track final results. We release our source code at:
https://github.com/knife982000/RecSys2023Challenge