Zhou, Xiaosong
(2018)
Understanding serendipity and its application in the context of information science and technology.
PhD thesis, University of Nottingham.
Abstract
Serendipity is widely experienced in current society, especially in the digital world. According to the Oxford Concise English Dictionary, the term “serendipity” is defined as “the occurrence and development of events by chance in a happy or beneficial way”. This PhD research project aims to understand serendipity in the context of information research, and then attempts to design information technologies which can support the encountering of serendipity in cyberspace.
The whole PhD project is organised with two parts. The first part investigates the nature of serendipity by conducting three user studies. After a systematic literature review on existing empirical studies of serendipity, the author finds there are research methodological problems in current studies; for example, the most widely used methods are those conventional ones like interview or survey, and it is mainly the subjective data that can be collected from participants. The author then conducted the first user study, which was an expert interview, where nine experts in the research area of serendipity were interviewed with a focus on the research methodological issues. This study successfully helped the author to gain a broader understanding of the advantages and disadvantages of employing different research methods in studying serendipity. Then the second user study, which was a diary-based study, was performed among a group of Chinese scholars with the aim to have a further investigation on the role of “context” played in the process of serendipity. The study lasted two weeks and successfully collected 62 serendipitous cases from 16 participants. The outcome of this study helped us with a better understanding of how these Chinese scholars experience serendipity, and a context-based research model was constructed, where the role of external context, social context and internal context were identified in detail during the process of serendipity. One interesting finding from the second user study is that emotions played a role in these participants’ experiencing serendipity, which was a part largely ignored by current serendipity researchers; therefore, the author conducted the third user study with the main objective to find out the impact of emotions during serendipitous encountering. This study first employed electrodermal activity (EDA) device to test participants’ psychological signals during the process of serendipity, which was implemented through a self-developed algorithm and the algorithm was embedded through a “Wizard of Oz” approach in a sketch game. The results of the study show that participants are more possible to experience serendipity under the influence of positive emotions and/or with skin conductance responses (SCRs).
The second part of the PhD project is the application of serendipity through recommendation technology. A recommender system is an important area that practises serendipity in the digital world, as users in today’s society are no longer satisfied with “accurate” recommendations, and they aim to be recommended with the information that is more serendipitous and interesting to them. However, a review of existing studies on serendipitous recommendation, I have found that the inspiring achievements of understanding the nature of serendipity from information science failed to gain attention by researchers in the area of recommender systems. I then developed a new serendipitous recommendation algorithm by adopting the theory of serendipity from information research and implemented the algorithm in a real data set. The algorithm was implemented in Movielens, which involves 138,493 users with about 20,000,263 ratings across 27,278 movies. The evaluation of the algorithm was conducted in a sub-dataset, which consists of 855,598 ratings from 2,113 users on 10,197 movies. The developed algorithm was compared with another two widely used collaborative filtering algorithms (user-based collaborative filtering and item-based collaborative filtering), and the results demonstrated the developed algorithm is more effective in recommending “unexpected” and “serendipitous” movies to users. A post user study on twelve movie scholars showed that these participants were possible to experience serendipity when they were recommended with movies under the developed algorithm; and compared to user-based collaborative filtering, these participants were more willing to follow the recommended use by the serendipitous algorithm.
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