Recommender systems are algorithmic tools that assist users in discovering relevant items from a wide range of available options. Along with the apparent user value in mitigating the choice overload, they have an important business value in boosting sales and customer retention. Last, but not least, they have brought a substantial research value to the algorithmdevelopments of the past two decades, mainly in the academic community. This thesis aims to address some of the aspects that are important to consider when recommender systems pave their way towards real-life applications.