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\paragraph{Minimally serendipitous systems.}
According to our standards, there are various ways to achieve a result
with \emph{low} serendipity: if the
outcome observation was
likely to happen, likely, if
it further
developments happened
by chance with little skill,
or and if the the value of the
result was low, then we would not say the outcome was serendipitous.
Campbell In Campbell's \citeyear{campbell}
describes description of serendipity as ``the
rational exploitation of chance observation, especially in the
discovery of something useful or
beneficial.'' beneficial'' one can see all the
components of our definition. We would be prepared to attribute
``minimal serendipity'' to cases where the observation was
\emph{moderately} likely, \emph{some} skill or effort was involved,
and the result was
only \emph{fairly good}. For computational
systems, if most of the skill involved lies with the user, then there
is little reason to call the system's operation serendipitous -- even
if it consistently does its job very well. For example, machines can
``learn'' to recognise instances of a given pattern quite
consistently, but it is
typically an interesting surprise if a computational
system independently finds an entirely new kind of pattern.
Furthermore, the position of the evaluator is important:
A a
spell-checking system might suggest a particularly fortuitous
substitution, but we would not expect the spell-checker to know when
it was being clever. In such a case, we may say serendipity has
...
As discussed in Section \ref{sec:related}, recommender systems are one
of the primary contexts in computing where serendipity is seen to play
a role. As we noted, these systems mostly focus on discovery.
Nevertheless, certain Although this describes the mainstream of recommender system
development, it seems that there are some architectures that also take
account of
invention would match all invention. We have in mind Bayesian methods (surveyed in
Chapter 3 of
criteria described by our model. Here we
draw \citeNP{shengbo-guo-thesis}). The current discussion
focuses on possibilities for serendipity on the system side, drawing
on the observation that recommender systems not \emph{stimulate}
serendipitous
discovery for the user: discovery: they also have the task of \emph{simulating}
when this is likely to occur.
A recommendation is typically provided if the system suspects that the
item will be likely to introduce ideas that are close to what the user
knows, but that will be unexpected.
In other words, the system aims
to stimulate Typical discussions of
serendipity
for the user. For example, a museum in recommender
service might suggest a colourful medieval painting to a systems focus on this. However, user
who seems to like colourful paintings by the modern artist Keith
Haring. User
behaviour (e.g.~following up on these recommendations)
is outside of the direct control of the system and may
also serve
as a \textbf{serendipity
trigger}, trigger} for the system, and change the way
it makes recommendations in the future.
The Note that it is typically the
system's \emph{developers} who adapt the system; even in the Bayesian
case, the system has
limited responsibilities. Nevertheless, the
impetus to develop increasingly autonomous systems is present,
especially in complex domains where hand-tuning reaches its limits.
Current systems have at least the makings of a \textbf{prepared mind},
including both a \emph{user model} and a \emph{domain model}, both of
which can be updated dynamically.
The connections through
which recommendations are made usually happen when the system notices
that elements of the domain have something in common via clustering or
faceting. A \textbf{bridge} to a new kind of
recommendation may be found
if by pattern matching, and especially by
looking for exceptional cases: new elements are introduced into the
domain which do not cluster well, or
if the user appears to know about different clusters
appear in the
user model that do not have obvious connections between them. The
intended outcome of recommendations
depend depends on the organisational
mission
e.g.~to mission: to make money, to provide a good user experience,
etc.; at the
system level, the etc. The
serendiptious \textbf{result} would be learning a new approach that
helps to address these
goals better.
From the perspective of our model, \textbf{chance} goals.
\textbf{Chance} will only have a significant role
when in the system
if it
has the capacity to learn from user behaviour.
In fact, Bayesian methods are used in contemporary
recommender systems (surveyed in Chapter 3 of
\citeNP{shengbo-guo-thesis}). %% The typical commercial perspective on recommendations is related to
%% the process of ``conversion'' -- turning recommendations into
%% clicks and clicks into purchases.
Combined with the ability to learn, \textbf{curiosity} could be
described as the urge to make
``outside-the-box''\footnote{\citeA{abbassi2009getting}.}
recommendations specifically for the purposes of learning more about
users, possibly to the detriment of other
goals metrics over the short term.
Measures of \textbf{sagacity} would relate to the system's ability to
draw inferences from user
behaviour that would update the
recommendation model. 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.
A \textbf{dynamic world} which nevertheless exhibits some regularity
is 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 based on the
results of these experiments. 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
% \emph{research and development} of recommender systems works, but
