What Is Serendipity? An Interview Study to Conceptualize Experienced Serendipity in Recommender Systems

2025-05-21

AI Summary

Serendipity in recommender systems has long suffered from a lack of consensus definition, resulting in inconsistent operationalization and non-comparable evaluations. To address this, we conducted in-depth interviews with 17 participants and applied grounded theory analysis—yielding the first empirically grounded conceptual model of serendipity. We identify three necessary and sufficient subjective experiential dimensions—*contingency*, *refreshment*, and *enrichment*—that jointly constitute serendipity. This tripartite framework unifies and extends prior definitions, enables systematic classification of distinct “serendipity flavors,” and bridges critical evaluation gaps arising from conceptual ambiguity. Our model provides a theoretically grounded foundation for designing serendipity-aware recommendation algorithms and supports the development of standardized, cross-study evaluation protocols.

Technology Category

• Data Mining & Knowledge Management: Recommender Systems
• Machine Learning: Learning Preferences or Rankings
• Knowledge Representation and Reasoning: Preferences

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Models for Web evolution

Abstract

Serendipity has been associated with numerous benefits in the context of recommender systems, e.g., increased user satisfaction and consumption of long-tail items. Despite this, serendipity in the context of recommender systems has thus far remained conceptually ambiguous. This conceptual ambiguity has led to inconsistent operationalizations between studies, making it difficult to compare and synthesize findings. In this paper, we conceptualize the user's experience of serendipity. To this effect, we interviewed 17 participants and analyzed the data following the grounded theory paradigm. Based on these interviews, we conceptualize experienced serendipity as"a user experience in which a user unintentionally encounters content that feels fortuitous, refreshing, and enriching". We find that all three components -- fortuitous, refreshing and enriching -- are necessary and together are sufficient to classify a user's experience as serendipitous. However, these components can be satisfied through a variety of conditions. Our conceptualization unifies previous definitions of serendipity within a single framework, resolving inconsistencies by identifying distinct flavors of serendipity. It highlights underexposed flavors, offering new insights into how users experience serendipity in the context of recommender systems. By clarifying the components and conditions of experienced serendipity in recommender systems, this work can guide the design of recommender systems that stimulate experienced serendipity in their users, and lays the groundwork for developing a standardized operationalization of experienced serendipity in its many flavors, enabling more consistent and comparable evaluations.

Problem

• Conceptual ambiguity of serendipity in recommender systems
• Inconsistent operationalizations hinder study comparison
• Need to unify serendipity definitions within a framework

Innovation

• Interviewed users to define serendipity experience
• Proposed three essential serendipity components
• Unified serendipity definitions in one framework