Techno-sociological Aspects of Automated Recommendations - Algorithms, Ethics and Evaluation

Dimitris Paraschakis defends his thesis

Opponent: Professor Christina Lioma, Copenhagen University

ABSTRACT

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. We begin our investigation by assessing the adoptability of popular recommendation algorithms by e-commerce platforms, and perform the comparative evaluation of these algorithms on real sales data provided by Apptus Technologies.

Based on the conducted survey and offline experiments, our research clarifies which algorithms are particularly useful for sales data. The realistic modeling and evaluation of recommender systems is another issue of utmost importance. Over the years, the field has been gradually moving away from the oversimplified matrix completion abstraction to more pragmatic modeling paradigms, such as sequential, streaming, and session-aware/session-based recommender systems (or all at once). Despite the rapidly increasing body of work in each of those directions, there is a need for more unified algorithmic solutions and evaluation frameworks supporting them.

To this end, we propose two recommender systems for streaming session data, as well as a new benchmarking/prototyping tool based on the streaming framework Scikit-Multiflow. Finally, a somewhat overlooked aspect of recommender systems is their ethical implications. When a recommender is intended to leave the lab and be deployed to real users, the purely accuracy-oriented algorithmic approach is no longer sufficient. A deployed recommender system must also guarantee its compliance with the societal and legal norms, such as anti-discrimination laws, GDPR, privacy, fairness, etc. To aid the development of ethics-aware recommender systems, we provide a holistic view on potential ethical issues that may arise at various stages of the development process, and advocate the provision of user-adjustable ethical filters. Among all ethical matters, algorithmic fairness stands on its own as a rapidly developing sub-field of machine learning, which has recently made its entry to the realm of recommender systems. We contribute to this research direction by formulating and solving the problem of preferentially fair matchmaking in speed dating with minimal accuracy compromises.