A comparative analysis of serendipity engines and personalized recommendations

This study investigates the influence of serendipity engines on consumer discovery behaviour, purchase diversity, and satisfaction within e-commerce platforms, contrasting their performance with traditional personalized recommendation systems. Unlike conventional algorithms that prioritize user experience by delivering tailored suggestions based on historical data, serendipity engines introduce an element of surprise, encouraging unexpected discoveries. By striking a balance between personalization and unpredictability, these engines have the potential to boost customer engagement, diversify purchasing patterns, and enhance overall satisfaction. Through a comparative analysis, the research evaluates the effectiveness of serendipity engines in creating a more dynamic and enriched shopping experience. The findings highlight their ability to foster broader consumer exploration, reduce the filter bubble effect associated with conventional systems, and contribute to more diverse purchase portfolios. This study offers valuable insights for optimizing recommendation strategies in e-commerce, aligning them with the evolving behaviours and preferences of modern consumers.