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 algorithm developments 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.

Recommender systems have become an integral part of virtually every e-commerce application on the web. These systems enable users to quickly discover relevant products, at the same time increasing business value. Over the past decades, recommender systems have been modeled using numerous machine learning techniques. However, the adoptability of these models by commercial applications remains unclear. We assess the receptiveness of the industrial sector to algorithmic contributions of the research community by surveying more than 30 e-commerce platforms, and experimenting with various recommenders on proprietary e-commerce datasets. Another overlooked but important factor that complicates the design and use of recommender systems is their ethical implications. We identify and summarize these issues in our ethical recommendation framework, which also enables users to control sensitive moral aspects of recommendations via the proposed “ethical toolbox”. The feasibility of this tool is supported by the results of our user study. Because of moral implications associated with user profiling, we investigate algorithms capable of generating user-agnostic recommendations. We propose an ensemble learning scheme based on Thompson Sampling bandit policy, which models arms as base recommendation functions. We show how to adapt this algorithm to realistic situations when neither arm availability nor reward stationarity is guaranteed.