In this paper, we describe the winning approach used on the RecSys Challenge 2014 which focuses on employing user en-gagement as evaluation of recommendations. On one hand, we regard the challenge as a ranking problem and apply the LambdaMART algorithm, which is a listwise model special-ized in a Learning To Rank approach. On the other hand, after noticing some specific characteristics of this challenge, we also consider it as a regression problem and use pointwise regression models such as Random Forests. We compare how these different methods can be modified or combined to improve the accuracy and robustness of our model and we draw the advantages or disadvantages of each approach.