Exploiting relevant dates to promote serendipity and situational curiosity in cultural heritage experiences

Dahroug, Ahmed; López‐Nores, Martín; Pazos‐Arias, José J.; Soutelo, Silvia González; Morillo, Susana Reboreda; Antoniou, Angeliki

doi:10.1109/smap.2017.8022673

Cited by 1 publication

(1 citation statement)

References 12 publications

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“…While most algorithms focus on the accuracy of the retrieved results, there has been increased interest in the development of algorithms that will go beyond accuracy to support novelty, diversity and serendipity with the retrieved results. Development of recommender systems that focus on providing ‘serendipitous’ encounters have been the focus of several information behavior studies (Erdelez, ; Makri, Toms, McCay‐Peet, & Blandford, ; McCay‐Peet & Toms, ; Wopereis & Braam, ) and human computer interaction studies (Dahroug et al, ; Fazeli et al, ; Maccatrozzo et al, ; Niu & Abbas, ) in recent years. One challenge to the development of recommender systems that support serendipity is identifying the type of information that end users will find surprising but also useful.…”

Section: Introductionmentioning

confidence: 99%

Distraction to illumination: Mining biomedical publications for serendipity in research

Allen

Erdelez

2018

Proc. Assoc. Info. Sci. Tech.

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As our technological capabilities for filling information needs improve, developers seek to more effective ways to support different aspects of the users' experience. One aspect that is gaining attention as an emerging support area is serendipity. However, supporting serendipity within a recommender system is difficult because the experience is unexpected and, therefore unpredictable. While researchers agree that algorithms to support serendipity need to be able to provide a balance of surprise and value to the end user (Niu & Abbas, 2017), an understanding of how to provide that balance has not yet been realized. Information that could be puzzling or distracting to someone as they go about their research activities may provide the trigger someone else needs to make a serendipitous connection in their research. Reports of serendipitous occurrences in research settings have been identified in research commentaries (Campanario, 1996) and within full‐text research articles (Allen, Erdelez, & Marinov, 2013). This paper investigates the feasibility of automating the identification of information encounters in full‐text research articles. This study contributes to the development of algorithms for supporting serendipity in information systems. We identified four variables that are useful for predicting information encounters in 25‐35% of the instances. While we should continue to search for additional predictive variables, these findings present a novel approach to undertaking the support of serendipity in information systems.

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