A new information theory-based serendipitous algorithm design

Zhou, Xiaosong; Xu, Zhan; Sun, Xu; Wang, Qingfeng

Abstract

The development of information technology has stimulated an increasing number of researchers to investigate how to provide serendipitous experience to users in the digital environment, especially in the fields of information research and recommendation systems. Although a number of achievements have been made in understanding the nature of serendipity in the context of information research, few of these achievements have been employed in the design of information systems. This paper proposes a new serendipitous recommendation algorithm based on previous empirical studies by taking into considerations of the three important elements of serendipity, namely “unexpectedness”, “insight” and “value”. We consider our design of the algorithm as an important attempt to bridge the research fruits between the two areas of information research and recommendation systems. By applying the designed algorithm to a game-based application in a real life experiment with target users, we have found that comparing to the conventional designed method; the proposed algorithm has successfully provided more possibilities to the participants to experience serendipitous encountering.

Citation

Zhou, X., Xu, Z., Sun, X., & Wang, Q. (in press). A new information theory-based serendipitous algorithm design. Lecture Notes in Artificial Intelligence, 10274, https://doi.org/10.1007/978-3-319-58524-6_26

• Journal Article Type: Article
• Acceptance Date: Jan 1, 2017
• Online Publication Date: May 18, 2017
• Deposit Date: Nov 3, 2017
• Journal: Lecture Notes in Artificial Intelligence
• Electronic ISSN: 0302-9743
• Publisher: Springer Verlag
• Peer Reviewed: Peer Reviewed
• Volume: 10274
• Book Title: Human Interface and the Management of Information: Supporting Learning, Decision-Making and Collaboration
• DOI: https://doi.org/10.1007/978-3-319-58524-6_26
• Keywords: Serendipity; Recommendation system; Information theory
• Public URL: https://nottingham-repository.worktribe.com/output/860993
• Publisher URL: https://link.springer.com/chapter/10.1007%2F978-3-319-58524-6_26
• Additional Information: The final publication is available at link.springer.com via http://dx.doi.org/10.1007%2F978-3-319-58524-6_26
• Contract Date: Oct 31, 2017