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On many of today's most popular Internet service platforms, users are confronted with a seemingly endless number of options to choose from, such as articles to purchase on online shopping sites, music to listen to on online streaming platforms, or posts to read on social media. As a solution to this choice overload problem, recommender systems have been integrated into more and more websites and applications to help users find items that they might like or that could be useful in their current choice situation. In recent decades, research on recommender systems has mostly been driven by offline performance comparisons, in which each new approach is compared to the state of the art in terms of its ability to retroactively predict user preferences in historical data sets. However, such a purely algorithmic research approach can only capture one of the many factors that contribute to a useful and engaging recommendation experience from a user perspective. In fact, a variety of aspects can influence how recommendations affect users' decision-making processes and how users perceive recommendations, including details regarding the recommender system's user interface or subconscious cognitive effects evoked by the recommendations. In this thesis by publication, selected works of the author are presented that investigate different aspects pertaining to the design and evaluation of recommender systems from a more user-focused perspective. The first part of the thesis outlines each of these publications and positions them within the research context. The presented works investigate (i) how recommender systems interact with their users, (ii) how recommender systems should be evaluated with the user in mind, (iii) possible biases in user studies, (iv) an algorithmic strategy to re-rank recommendation lists according to individual user tendencies, and (v) two phenomena based on which recommendations can subconsciously influence user decision-making processes. The second part of the thesis, the appendix, contains the aforementioned publications in full. The presented studies demonstrate that it is imperative to design and evaluate recommender systems with the user in mind, taking into account the intricacies of interaction details, recommendation list composition, user context, and decision-making processes. |
