Serendipity in Recommender Systems Beyond the Algorithm: A Feature Repository and Experimental Design


Serendipity in recommender systems is ought to improve the quality and usefulness of recommenda- tions. However, despite the increasing amount of attention in both research and practice, designing for serendipity in recommenders continues to be challenging. We argue that this is due to the narrow interpretation of serendipity as an evaluation metric for algorithmic performance. Instead, we venture that serendipity in recommenders should be understood as a user experience that can be influenced by a broad range of system features that go beyond mere algorithmic improvements. In this paper, we propose a first feature repository for serendipity in recommender systems that identifies which elements could theoretically contribute to serendipitous encounters. These include design aspects related to the content, user interface and information access. Furthermore, we outline an experimental design for evaluating the influence of these features on the serendipitous encounters by users. The experiment design is described in such a way that it can be easily reproduced in different recommendation scenarios to contribute empirical insights in various settings. This work aspires to represent a first step towards fostering a more integrated and user-centric view on serendipity in recommender systems and thereby improving our ability to design for it.

Proceedings of the 9th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems co-located with 16th ACM Conference on Recommender Systems (RecSys 2022)
Annelien Smets
Senior Researcher

Annelien’s research centers around personalization and recommender systems, and their value in media markets. She holds a PhD in Media and Communication Studies on the topic of serendipity in recommender systems and smart cities.