Databases Reference
In-Depth Information
information about individuals, to facilitate collaboration and flow of information, and
predict behavior.
As Facebook's use of the term
social graph
implies, graph data models and graph data‐
bases are a natural fit for this overtly relationship-centered domain. Social networks
help us identify the direct
and
indirect relationships between people, groups, and the
things with which they interact, allowing users to rate, review, and discover each other
and the things they care about. By understanding who interacts with whom, how people
are connected, and what representatives within a group are likely to do or choose based
on the aggregate behavior of the group, we generate tremendous insight into the unseen
forces that influence individual behaviors. We discuss predictive modeling and its role
in social network analysis in more detail in
“Graph Theory and Predictive Modeling”
on page 174
.
Social relations may be either explicit or implicit. Explicit relations occur wherever social
subjects volunteer a direct link—by liking someone on Facebook, for example, or in‐
dicating someone is a current or former colleague, as happens on LinkedIn. Implicit
relations emerge out of other relationships that indirectly connect two or more subjects
by way of an intermediary. We can relate subjects based on their opinions, likes, pur‐
chases, and even the products of their day-to-day work. Such indirect relationships lend
themselves to being applied in multiple suggestive and inferential ways: we can say that
A is
likely
to know, or like, or otherwise connect to B based on some common inter‐
mediaries. In so doing, we move from social network analysis into the realm of recom‐
mendation engines.
Recommendations
Effective recommendations are a prime example of generating end-user value through
the application of an inferential or suggestive capability. Whereas line-of-business ap‐
plications typically apply deductive and precise algorithms—calculating payroll, apply‐
ing tax, and so on—to generate end-user value, recommendation algorithms are in‐
ductive and suggestive, identifying people, products, or services an individual or group
is
likely
to have some interest in.
Recommendation algorithms establish relationships between people and things: other
people, products, services, media content—whatever is relevant to the domain in which
the recommendation is employed. Relationships are established based on users' behav‐
iors, whether purchasing, producing, consuming, rating, or reviewing the resources in
question. The engine can then identify resources of interest to a particular individual
or group, or individuals and groups likely to have some interest in a particular resource.
With the first approach, identifying resources of interest to a specific user, the behavior
of the user in question—her purchasing behavior, expressed preferences, and attitudes
as expressed in ratings and reviews—are correlated with those of other users in order
to identify similar users and thereafter the things with which they are connected. The
