Course project for Computational Intelligence Lab at ETH Zurich in the spring of 2018 in collaboration with Ben Hahn and Kevin Klein.
Online businesses face the challenge of recommending relevant products to users based on users’ previous preferences and similar customers. This work explores the use of classic matrix factorization methods on the one hand and recent neural network-based methods on the other hand. Final predictions were further improved using ensembling methods such as bagging and stacking. We report similar, competitive scores for matrix factorization methods and slightly lower accuracy for neural network-based methods with a final ensemble RMSE of 0.964.
My team mate, Kevin Klein, nicely summarises his main insights here.