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1952, defined it solely as an event, while five define it as both  event and attribute.  An active research community investigating computational models of serendipity can be found in information retrieval \cite{Toms2000} and more specifically, in recommender systems. In the latter domain, Herlocker et al. \cite{Herlocker2004} and especially McNee et al. \cite{McNee2006} promoted serendipity as an important factor for user satisfaction, next to accuracy and diversity. There are several definitions of serendipity in this domain \cite{Lu2012}, which commonly require the system to recommend an unexpected and useful \cite{Lu2012}, interesting \cite{Herlocker2004}, attractive or relevant \cite{Ge2010} item. In terms of the prior definitions, the problem can be framed as the user's difficulty in finding items that meet his or her interests or demands within a large and potentially unobservable search space. This problem can also be passive, and items are suggested to support other stakeholder's goals, e.g. to increase sells. Definitions differ in the requirement for novelty, and some researchers \cite{Adamopoulos2011} develop systems for suggesting items that might already be known, but are still unexpected in the current context.   There are numerous examples that exhibit features of  serendipity which develop on a social scale rather than an individual  scale. For instance, between Spencer Silver's creation of high-tack,  diff --git a/related-work.tex b/related-work.tex  index f0c8985..09e578d 100644  --- a/related-work.tex  +++ b/related-work.tex 
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\subsection{Related work} \label{sec:related}  An active research community investigating computational models of serendipity can be found in information retrieval \cite{Toms2000} and more specifically, in recommender systems. In the latter domain, Herlocker et al. \cite{Herlocker2004} and especially McNee et al. \cite{McNee2006} promoted serendipity as an important factor for user satisfaction, next to accuracy and diversity. There are several definitions of serendipity in this domain \cite{Lu2012}, which commonly require the system to recommend an unexpected and useful \cite{Lu2012}, interesting \cite{Herlocker2004}, attractive or relevant \cite{Ge2010} item. In terms of the prior definitions, the problem can be framed as the user's difficulty in finding items that meet his or her interests or demands within a large and potentially unobservable search space. This problem can also be passive, and items are suggested to support other stakeholder's goals, e.g. to increase sells. Definitions differ in the requirement for novelty, and some researchers \cite{Adamopoulos2011} develop systems for suggesting items that might already be known, but are still unexpected in the current context.  Paul Andr{\'e} et al.~\citeyear{andre2009discovery} look at  serendipity from a design perspective. These authors also propose a  two-part model, in which what we have called \emph{discovery} above