Personalized Beyond-accuracy Calibration in Recommendation

Recommender systems usually aim to optimize accuracy in a supervised setting. Due to various data biases, they often fail to enhance other critical qualities that go beyond accuracy, such as diversity, novelty, and serendipity. Prior studies focus on addressing the bias in beyond-accuracy metrics from the provider's perspective, such as increasing the overall diversity of recommendations to combat popularity bias. In this work, we take a user-centric approach to this problem and demonstrate that users have distinct preferences for beyond-accuracy metrics. We hypothesize that users have an implicit behavioral model that goes beyond optimizing their choices only for accuracy. For instance, we assume that a user's purchase behavior is a mix of items that are more familiar to the user (optimizing for accuracy), and new items that are aimed for exploration (optimizing for novelty). We argue that a recommender system should reflect users' interest in such beyond-accuracy metrics. This perspective allows for a more holistic understanding of users' behavior and preferences leading to more fine-grained personalized recommendations. To this end, we propose a post-ranking greedy optimization algorithm that ensures recommendations are not only accurate but also meet users' beyond-accuracy preferences. Through extensive experiments, we demonstrate our proposed method's ability to balance the trade-off between ranking accuracy and user-centric beyond-accuracy preferences.