Implementing and Evaluating Serendipity in Delivering Personalized Health Information

Abstract

Serendipity has been recognized to have the potential of enhancing unexpected information discovery. This study shows that decomposing the concept of serendipity into unexpectedness and interest is a useful way for implementing this concept. Experts’ domain knowledge helps in providing serendipitous recommendation, which can be further improved by adaptively incorporating users’ real-time feedback. This research also conducts an empirical user-study to analyze the influence of serendipity in a health news delivery context. A personalized filtering system named MedSDFilter was developed, on top of which serendipitous recommendation was implemented using three approaches: random, static-knowledge-based, and adaptive-knowledge-based models. The three different models were compared. The results indicate that the adaptive-knowledge-based method has the highest ability in helping people discover unexpected and interesting contents. The insights of the research will make researchers and practitioners rethink the way in which search engines and recommender systems operate to address the challenges of discovering unexpected and interesting information. The outcome will have implications for empowering ordinary people with more chances of bumping into beneficial information.