Search Results for author:
Found 2 papers, 0 papers with code
Modelling customer churn for the retail industry in a deep learning based sequential framework
As retailers around the world increase efforts in developing targeted marketing campaigns for different audiences, predicting accurately which customers are most likely to churn ahead of time is crucial for marketing teams in order to increase business profits.
Sequence-aware item recommendations for multiply repeated user-item interactions
Recommender systems are one of the most successful applications of machine learning and data science.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
