deletions | additions       diff --git a/bibliography/biblio.bib b/bibliography/biblio.bib  index 26f6086..85bc556 100644  --- a/bibliography/biblio.bib  +++ b/bibliography/biblio.bib 
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@article{rubin2010everyday,  title={Everyday serendipity as described in social media},  author={Rubin, Victoria L and Burkell, Jacquelyn and Quan-Haase, Anabel},  journal={Proceedings of the American Society for Information Science and Technology},  volume={47},  number={1},  pages={1--2},  year={2010},  publisher={Wiley Online Library}  }  @article{landovitz2013epidemiology,  title={Epidemiology, sexual risk behavior, and HIV prevention practices of men who have sex with men using GRINDR in Los Angeles, California},  author={Landovitz, Raphael J and Tseng, Chi-Hong and Weissman, Matthew and Haymer, Michael and Mendenhall, Brett and Rogers, Kathryn and Veniegas, Rosemary and Gorbach, Pamina M and Reback, Cathy J and Shoptaw, Steven},  journal={Journal of Urban Health},  volume={90},  number={4},  pages={729--739},  year={2013},  publisher={Springer}  }  @inbook{lscitter,  title = "L{S}{C}itter: building social machines by augmenting existing social networks with interaction models",  publisher = "ACM",  author = "Dave Murray-Rust and Dave Robertson",  year = "2014",  doi = "10.1145/2567948.2578832",  isbn = "978-1-4503-2745-9",  pages = "875-880",  booktitle = "Proceedings of the companion publication of the 23rd international conference on World wide web companion",  }  @article{eagle2005social,  title={Social serendipity: Mobilizing social software},  author={Eagle, Nathan and Pentland, Alex},  journal={Pervasive Computing, IEEE},  volume={4},  number={2},  pages={28--34},  year={2005},  publisher={IEEE}  }  @incollection{robot-rendezvous,  year={2014},  isbn={978-3-319-08815-0},  diff --git a/related-work.tex b/related-work.tex  index fe32829..d198421 100644  --- a/related-work.tex  +++ b/related-work.tex 
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An active research community investigating computational models of serendipity exists in the field of information retrieval, and specifically, in recommender systems \cite{Toms2000}. In this domain, \citeA{Herlocker2004} and \citeA{McNee2006} view serendipity as an important factor for user satisfaction, next to accuracy and diversity. Serendipity in recommendations variously require the system to deliver an \emph{unexpected} and \emph{useful} \cite{Lu2012}, \emph{interesting} \cite{Herlocker2004}, \emph{attractive} or \emph{relevant} item \cite{Ge2010}.   %% Recommendations are typically meant to help address the user's difficulty in finding items that meet his or her interests or demands within a large and potentially unobservable search space. The end user can also be passive, and items are suggested to support other stakeholder's goals, e.g. to increase sells.   Definitions differ as to the requirement of \emph{novelty}; \citeA{Adamopoulos2011}, for example, describe systems that suggest items that may already be known, but are still unexpected in the current context. While standardized measures such as the $F_1$-score or the (R)MSE are used to determine the accuracy of an evaluation in terms of preferred items in the user's history, there is no common agreement on a measure for serendipity yet, although there are several proposals \cite{Murakami2008, Adamopoulos2011, McCay-Peet2011}.  In terms of our model, these systems focus mainly on producing a serendipity trigger, \emph{serendipity trigger} for the user,  but they include aspects of user modeling which could bring other elements into play. Paul Andr{\'e} et al.~\citeyear{andre2009discovery} have examined  serendipity from a design perspective. These authors also propose a 
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are made about the potential serendipity path.'' This has some  similarity to the discursive scenario described above, and shows that  \emph{asymmetric partial knowledge} can support serendipitious  findings. The These examples suggest that a  distinction between emergent  knowledge of other actors and knowledge about an underlying domain may  be useful -- although the distinction would somewhat less relevant if  the underlying domain itself has dynamic and emergent features.  Social coordination among human users of information systems  is useful. a  current research topic \cite{lscitter}. \citeA{rubin2010everyday}  point out that naive end users often \emph{talk about} serendiptious  occurrences, which presents another route for research and evaluation.  The issue of designing for serendipity has been taken up recently {\sf SerenA} system developed  by Deborah Maxwell et al.~\citeyear{maxwell2012designing}, in their  description of al.~\citeyear{maxwell2012designing} offers  a prototype case study of several  of the {\sf SerenA} system. points discussed above.  This system is designed to support serendipitous discovery for its (human) users \cite{forth2013serena}. The authors rely on a process-based model of  serendipity \cite{Makri2012,Makri2012a} that is derived from user  studies, including interviews with 28 researchers, looking for 
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\emph{unexpectedness}, \emph{insightfulness}, and \emph{value}. This  research aims to support the process of forming bridging connections  from unexpected encounter to a previously unanticipated but valuable  outcome. They particularly focus The theory focuses  on the acts of \emph{reflection} that foment both the creation of a bridge and estimates of the  potential value of the result.  %  Although While  this description touches on all of the features of our model, {\sf SerenA} largely matches the description offered by Andr{\'e} et  al.~\citeyear{andre2009discovery} of discovery-focused systems,and a  case study of a recommender system focused on serendipity,  in which%% Here, the  %% user is the primary agent with a prepared mind. Accordingly it  is user experiences an ``aha'' moment and takes the  creative steps to realise the result. {\sf SerenA}'s primary computational method is to  search outside of the normal search parameters in order to engineer