The Dark Matter of Serendipity in Recommender Systems

Serendipity has been recognized as a valuable property of recommender systems. While there is a lack of consensus on the precise definition of serendipity, it is often conceptualized in terms of the relevance, novelty and unexpectedness of recommendations. However, the common understanding and original meaning of serendipity is conceptually broader, requiring serendipitous encounters to be neither novel nor unexpected. Recent work has highlighted the various ways in which serendipity can manifest, leading to a more generalized definition of serendipity. In this paper, we conducted an observational study where we collected 2002 survey responses from 397 users of an online article recommender system. In our study, we found a significant proportion of serendipitous recommendations were missed by the conventional definitions used in the recommender systems research literature, exposing the "dark matter"of serendipity that has been overlooked in prior studies. Interestingly, users' opinions of which articles should be considered serendipitous did not strongly align with any of the definitions investigated. Furthermore, despite several user behaviors being significantly associated with a majority of definitions of serendipity, the overall goodness of fit was very low. Our findings highlight the issues of evaluating serendipity in recommender systems and the challenge of reconciling serendipity with user expectations.