A Framework for Computational Serendipity

Abstract

In this paper, we propose a framework for computational serendipity. The framework is used in a recommender system context to find personalized serendipity and meanwhile stimulate user's curiosity. The framework is novel to the serendipity research community in that it decomposes the concept of serendipity into two elements: surprise and value; and provides computational approaches to modeling both of them. The framework also incorporates the concept of curiosity to keep users' interests over a long term. It brings together several fields including information retrieval, cognitive science, computational creativity in artificial intelligence, and text mining. We will describe the framework first and then evaluate it with an implementation called StumbleOn in the health news context. The evaluation serves as a proof-of-concept of this computational serendipity framework.