#30: Serendipity for Recommender Systems with Annelien Smets

In episode 30 of Recsperts, I speak with Annelien Smets, Professor at Vrije Universiteit Brussel and Senior Researcher at imec-SMIT, about the value, perception, and practical design of serendipity in recommender systems. Annelien introduces her framework for understanding serendipity through intention, experience, and affordances, and explains the paradox of artificial serendipity - why it cannot be engineered, but only designed for.

We start by unpacking the paradox of serendipity: while serendipity cannot be engineered or planned, systems and environments can be designed to increase the likelihood that serendipitous experiences occur. Annelien explains why randomness alone is not enough and why serendipity always emerges from an interplay between an unexpected encounter and a user’s ability to recognize its relevance and value.

A central part of our discussion focuses on Annelien’s recent framework that distinguishes between intended, experienced, and afforded serendipity. We explore why organizations first need to clarify why they want serendipity - whether as an ideal, a common good, a mediator to achieve other goals (such as long-term retention or long-tail exposure), or even as a product feature in itself. From there, we dive into how users actually experience serendipity, drawing on qualitative interview research that identifies three core components: encounters must feel fortuitous, refreshing, and enriching. These components can manifest in different “flavors,” such as taste broadening, taste deepening, or rediscovering forgotten interests.

We then move beyond algorithms to discuss affordances for serendipity - design principles that span content, user interfaces, and information access. Using examples from libraries, urban spaces, and digital platforms, Annelien shows why serendipity is a system-level property rather than a single metric or model tweak. We also discuss where serendipity can go wrong, including the Netflix “Surprise Me” feature, and why mismatched expectations can actually harm user experience.

To close, we reflect on open research questions, from measuring different types of serendipity to understanding how content types, business models, and platform economics shape what is possible. Annelien also challenges a common myth: serendipity does not automatically burst filter bubbles—and should not be treated as a silver bullet.

Enjoy this enriching episode of RECSPERTS – Recommender Systems Experts.

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• (00:00) - Introduction
• (03:57) - About Annelien Smets
• (14:42) - Paradox and Definition of (Artificial) Serendipity
• (27:04) - Intended Serendipity
• (43:01) - Experienced Serendipity
• (01:01:18) - Afforded Serendipity
• (01:13:49) - Examples of Serendipity Going Wrong
• (01:17:40) - Framework for Serendipity
• (01:22:41) - Further Challenges and Closing Remarks

• Annelien Smets on LinkedIn
• Website of Annelien
• LinkedIn Article by Annelien Smets (2025): Overcoming the Paradox of Artificial Serendipity
• The Serendipity Society
• Serendipity Engine

Papers:

• Smets (2025): Intended, afforded, and experienced serendipity: overcoming the paradox of artificial serendipity
• Smets et al. (2022): Serendipity in Recommender Systems Beyond the Algorithm: A Feature Repository and Experimental Design
• Binst et al. (2025): What Is Serendipity? An Interview Study to Conceptualize Experienced Serendipity in Recommender Systems
• Ziarani et al. (2021): Serendipity in Recommender Systems: A Systematic Literature Review
• Chen et al. (2021): Values of User Exploration in Recommender Systems
• Smets et al. (2025): Why Do Recommenders Recommend? Three Waves of Research Perspectives on Recommender Systems
• Smets (2023): Designing for Serendipity, a Means or an End?

General Links:

• Follow me on LinkedIn
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• Send me your comments, questions and suggestions to marcel.kurovski@gmail.com
• Recsperts Website