Augmenting micro-moment recommendations with group and serendipity perspectives

Abstract

With the pervasive integration of internet and mobile services, mobile devices have become integral to daily life. The concept of micro-moments, characterized by immediate intent within specific contexts, underscores the importance of timely and relevant information. Traditional RS, though effective in mitigating information overload, often fall short in addressing the dynamic and context-specific needs inherent in micromoments. This study investigates the enhancement of MMRS by incorporating group dynamics and serendipity, aiming to improve recommendation quality and user satisfaction. The research explores two primary objectives: the feasibility of a groupaugmented MMRS and the integration of serendipity into MMRS. Utilizing a design science approach, we conducted a two-phase iterative design involving preliminary studies and field experiments. The results indicate that integrating group recommendations based on social relationships and serendipity mechanisms significantly enhances user satisfaction and behavioral intentions. Close groups exhibited higher satisfaction and engagement compared to acquainted groups, emphasizing the importance of social relationships in recommendation strategies. Moreover, the serendipity mechanism, characterized by relevance, novelty, and unexpectedness, successfully mitigates overspecialization, enriching user experience by introducing unexpected yet relevant recommendations. Our findings contribute to the theoretical understanding of MMRS by demonstrating the viability of combining group dynamics and serendipity to cater to the evolving needs of mobile users in micro-moments. Practically, the study provides valuable insights for developing RS that are adaptive, context-aware, and capable of delivering engaging and satisfying user experiences. Future research should expand on diverse social relationships and longterm evaluations to refine the application of these mechanisms in various domains.