Insight Generation and Recommendation: Driving Behavior and Systemic Change

In the rapidly evolving domain of Data Science, the term "insights" takes center stage as one of the most commonly used yet often misunderstood expressions. These insights are more than mere statements; they serve as illuminating revelations that unravel intricate patterns and behaviors concealed within the vast expanse of data. By gaining a thorough understanding of these insights, individuals can decipher complex patterns hidden in data that could be about themselves or larger systems they manage. This clarity, in turn, empowers them to make informed decisions, avoiding the pitfalls of common misconceptions, and propelling them toward achieving their goals. Presenting insights in a clear and accessible way is essential for connecting complex data patterns with individuals who may find detailed information challenging to understand. However, the process extends beyond the communication of these insights; it is equally essential to intelligently recommend the right insights at the right time. This all-encompassing approach ensures that the value extracted from data transcends its intricacies and is seamlessly transformed into practical and easily comprehensible guidance for individuals. This dissertation presents a framework for mining and recommending insights, examining their benefits in personal health and enabling individuals to make optimal choices to achieve their goals. It explores the adaptability of recommendations to the dynamic preferences of users through feedback. Additionally, the dissertation investigates the potential of preliminary testing of the framework and its impact on behavior change through simulations, laying the groundwork for field trials. Finally, it demonstrates the effective application of the proposed framework in various use cases involving structured or unstructured data, including lifestyle intervention, hospital management and opinion mining.