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and his dog's fur and became curious about how they worked. After
examining them under a microscope, he realised the possibility of
creating a new kind of fastener that worked in a similar
fashion, laying the foundations for the
hook-and-loop hook-and-pile fastener.
% \cite[p. x]{roberts}
\end{itemize}
...
\item \textbf{Bridge}: These include reasoning techniques, such as
abductive inference (what might cause a clear patch in a petri
dish?); analogical reasoning (de Mestral constructed a target domain
from the source domain of
burrs burs hooked onto fabric); and conceptual
blending (Kekul\'e, discoverer of the benzene ring structure, blended his knowledge of molecule structure with
his vision of a snake biting its tail). The bridge may also rely on
new social arrangements, such as the formation of cross-cultural
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\paragraph{Recommender systems.}
% Stress distinction between serendipity on the system- vs. serendipity on the user's side.
As discussed in Section \ref{sec:related}, recommender systems are one
of the primary contexts in computing where serendipity is
seen addressed. 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
play what the user is already interested in. If the latter connection exists, such a
role. system must be called pseudoserendipitous. As we noted, these systems mostly focus on
discovery.
Although this describes the mainstream of recommender system
development, it seems that there are discovery, but some architectures
that also
seem to take account of
invention invention, such as the Bayesian methods surveyed in Chapter 3 of \citeNP{shengbo-guo-thesis}.
The current discussion
focuses on possibilities for serendipity on the system side, drawing
on the observation that recommender Recommender systems
do not only \emph{stimulate} serendipitous discovery,
but also \emph{simulate} by \emph{simulating} when this is likely to occur.
Most discussions In respect to related work, we therefore have to distinguish serendipity on the side of
the user from serendipity in
recommender systems the system.
Current research mainly focusses on the first aspect. The present approaches focus on
suggesting finding and assessing \textbf{serendipity triggers} by exploiting patterns in the search space. For example, \cite{Herlocker2004} as well as \cite{Lu2012} associate less popular items
to with a
higher unexpectedness. Clustering was also frequently used to discover latent structures in the search space. For example, \cite{Kamahara2005} partition users into clusters of common interest, while \cite{Onuma2009} as well as \cite{Zhang2011} perform clustering on both users and items. In the work by \cite{Oku2011}, the user
that will be likely is allowed to
introduce new ideas that are unexpected, but close select two items in order to
what mix their features in some sort of conceptual blending.
Note that it is typically the
user system's \emph{developers} who adapt the system; even in the Bayesian case, the system has limited autonomy. Nevertheless, the impetus to develop increasingly autonomous systems is
already interested in. If present, especially in complex domains where hand-tuning is either very cost-intensive or infeasible. In the context of this paper, we therefore want to investigate how serendipity could be achieved on the
latter connection exists, such a system
must side, and potentially be
called pseudoserendipitous. Current reflected back to the user. In terms of our model, current systems have at least the makings of a \textbf{prepared mind},
including comprising both a
\emph{user model} user- and a
\emph{domain model}, 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: 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
serendiptious serendipitous \textbf{result}
on the system side would
be learning resemble a
new novel learnt approach that helps to address these goals.
\textbf{Chance} will only have a significant role in the system if it
has the capacity to learn from user behaviour.
%% The typical commercial perspective on recommendations is related to
%% the process of ``conversion'' -- turning recommendations into
%% clicks and clicks into purchases.
Note that it is typically the system's \emph{developers} who adapt the system; even in the Bayesian case, the system has limited autonomy. Nevertheless, the impetus to develop increasingly autonomous systems is present, especially in complex domains where hand-tuning is either very cost-intensive or infeasible. As an approach towards more autonomy, 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. Combined with the ability to learn, \textbf{curiosity} could be described as the urge to make recommendations specifically for the purposes of learning more about users, possibly to the detriment of other metrics over the short term. Measures of \textbf{sagacity} would relate to the system's ability to
draw inferences from user behaviour. For example, the system might do A/B testing to decide how novel recommendation strategies influence conversion. Again, currently this would typically be organised by the system's developers. The \textbf{value} of recommendation strategies
can be measured in terms of traditional business metrics or other organisational objectives.
%%%
\begin{table}[ht!] \begin{table}[Ht!]
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\begin{tabular}{p{.7in}@{\hspace{.1in}}p{1.9in}@{\hspace{.1in}}p{1.9in}}
...
\normalsize
%%%
A The imperfect knowledge about the user's preferences and interests represents a main component of \emph{chance}. Furthermore, chance can play an important role if a system had the capacity to learn from user behaviour. Combined with the ability to learn, \textbf{curiosity} could be described as the urge to make recommendations specifically for the purposes of learning more about users, possibly to the detriment of other metrics over the short term. Measures of \textbf{sagacity} would relate to the system's ability to draw inferences from user behaviour. For example, the system might do A/B testing to decide how novel recommendation strategies influence conversion. Again, currently this would typically be organised by the system's developers. The \textbf{value} of recommendation strategies can be measured in terms of traditional business metrics or other organisational objectives.
Recommender systems have to cope with a \textbf{dynamic world}
of changing user preference ratings and new items in the system. At the same time, such a dynamic environment which nevertheless exhibits some regularity
is represents a precondition for useful A/B testing. As mentioned above the primary \textbf{(multiple) contexts} are the user model and the domain model. A system matching the description here would have \textbf{multiple tasks}: making useful recommendations, generating new experiments to learn about users, and building new models. Such a system could avail itself of \textbf{multiple influences} related to
experimental design, psychology, and domain understanding.
% As a general comment, we would say that this is largely how
