Lightweight Boosting Models for User Response Prediction Using
  Adversarial Validation
Hyeonwoo Kim Wonsung Lee
The ACM RecSys Challenge 2023, organized by ShareChat, aims to predict the
probability of the app being installed. This paper describes the lightweight
solution to this challenge. We formulate the task as a user response prediction
task. For rapid prototyping for the task, we propose a lightweight solution
including the following steps: 1) using adversarial validation, we effectively
eliminate uninformative features from a dataset; 2) to address noisy continuous
features and categorical features with a large number of unique values, we
employ feature engineering techniques.; 3) we leverage Gradient Boosted
Decision Trees (GBDT) for their exceptional performance and scalability. The
experiments show that a single LightGBM model, without additional ensembling,
performs quite well. Our team achieved ninth place in the challenge with the
final leaderboard score of 6.059065. Code for our approach can be found here:
https://github.com/choco9966/recsys-challenge-2023.