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As discussed in Section \ref{sec:related}, recommender systems are one  of the primary contexts in computing where serendipity is considered. Most discussions of serendipity in recommender systems focus on suggesting items to a user that will be likely to introduce new ideas that are unexpected, but close to what the user is already interested in. A recommendation of this type will be called (possibly pseudo-)serendipitous. As we noted, these systems mostly focus on supporting discovery, but some architectures also seem to take account of invention, such as the Bayesian methods surveyed in Chapter 3 of \citeNP{shengbo-guo-thesis}. Recommender systems \emph{stimulate} serendipitous discovery, by \emph{simulating} when this is likely to occur. In respect to related work, we therefore have to distinguish serendipity on the the user side from serendipity in the system.   As we have indicated, most current research in this area focuses on  the first aspect and tries to find and assess \textbf{serendipity triggers} by exploiting patterns in the search space. For example, \citeA{Herlocker2004} as well as \citeA{Lu2012} associate less popular items with high unexpectedness. Clustering is also frequently used to discover latent structures in the search space. For example, \citeA{Kamahara2005} partition users into clusters of common interest, while \citeA{Onuma2009} as well as \citeA{Zhang2011} perform clustering on both users and items. In the work by \citeA{Oku2011}, the user is allowed to select two items in order to mix their features in a sort of conceptual blend. Note that in the course of evolution of these and other systems it is generally the system's developers who plan and perform adaptations; even in the Bayesian case, the system has limited autonomy. Nevertheless, the impetus to develop increasingly autonomous recommender systems is present, especially in complex domains where hand-tuning is either very cost-intensive or infeasible. With this challenge in mind, we investigate how serendipity could be achieved on the system side, and potentially be reflected back to the user. In terms of our model, current systems have at least the makings of a \textbf{prepared mind}, comprising both a user- and a domain model, both of which can be updated dynamically. User behaviour (e.g.~following up on these recommendations) may serve as a \textbf{serendipity trigger} for the system, and change the way it makes recommendations in the future. A \textbf{bridge} to a new kind of recommendation may be found by pattern matching, and especially by looking for exceptional cases: when new elements are introduced into the domain which do not cluster well, or different clusters appear in the user model that do not have obvious connections between them. The intended outcome of recommendations depends on the organisational mission, and can in most cases be situated between making money and empowering the user. The serendipitous \textbf{result} on the system side would be learning a new approach that helps to address these goals.