Top-N recommenders are systems that provide a ranked list of N products to every user; the recommendations are of items that the user will potentially like. Top-N recommendation systems are present everywhere and used by millions of users, as they enable them to quickly find items they are interested in, without having to browse or search through big datasets; an often impossible task. The quality of the recommendations is crucial, as it determines the usefulness of the recommender to the users. So, how do we decide which products should be recommended? Also, how do we address the limitations of current approaches, in order to achieve better quality? In order to provide insight into these problems, this thesis focuses on developing novel, scalable algorithms that improve the state-of-the-art top-N recommendation quality, while providing insight into the top-N recommendation task. The developed algorithms address some of the limitations of existent top-N recommendation approaches and can be applied to real-world problems and datasets. The main areas of our contributions are the following: 1. Exploiting higher-order sets of items: We investigate to what extent higher-order sets of items are present in real-world datasets, beyond pairs of items. We also show how to best utilize them to improve the top-N recommendation quality. 2. Estimating a global and multiple local models: We show that estimating multiple user-subset specific local models, beyond a global model significantly improves the top-N recommendation quality. We demonstrate this with both item-item models and latent space models. 3. Investigating and using the error: We investigate what are the properties of the error and how they correlate with the top-N recommendation quality, in methods that treat the missing entries as zeros. Then, we utilize the learned insights to develop a method, which explicitly uses the error. We have applied our algorithms to big datasets, with millions of ratings, that span different areas, such as grocery transactions, movie ratings, and retail transactions, showing significant improvements over the state-of-the-art.