Rethinking Serendipity in Recommender Systems

Recommender systems suggest items, such as movies or books, to users based on their interests. These systems often suggest items that users are either already familiar with or could easily have found on their own without additional assistance. To overcome these problems, recommender systems aim to suggest serendipitous items. While there is a lack of consensus in the recommender systems research community on the definition of serendipity, it is often conceptualized as a complex combination of relevance, novelty and unexpectedness. However, the common understanding and original meaning of serendipity is conceptually broader, requiring serendipitous encounters to be neither novel nor unexpected. Recent work in the social sciences has highlighted the various ways that serendipity can manifest, leading to a more generalized definition of serendipity. We argue that the study of serendipity in recommender systems would benefit from considering items that are serendipitous under this more general definition, giving us a deeper understanding of the item characteristics and behavioral impact of serendipitous recommendations. These findings will help us to better optimize recommender systems for serendipity. In this paper, we explore various definitions of serendipity and propose a novel formalization of what it means for recommendations to be serendipitous. Lastly, we present an experimental design for how serendipity can be measured in a deployed recommender system.