  This paper describes the approach proposed by the D2KLab team for the 2020
RecSys Challenge on the task of predicting user engagement facing tweets. This
approach relies on two distinct stages. First, relevant features are learned
from the challenge dataset. These features are heterogeneous and are the
results of different learning modules such as handcrafted features, knowledge
graph embeddings, sentiment analysis features and BERT word embeddings. Second,
these features are provided in input to an ensemble system based on XGBoost.
This approach, only trained on a subset of the entire challenge dataset, ranked
22 in the final leaderboard.
