Database Reference
In-Depth Information
Deciding what you like, what you don't, and in some cases what you are
indifferent to drives nearly all the purchasing decisions you'll make through
your lifetime. Successfully predicting these outcomes is the basis of a
recommendation engine.
To set a foundation, let's define what and how a recommendation engine
works. First we must start with an assumption that people with similar
interests share common preferences. This is a straightforward idea
demonstrable by simply looking around at your network of friends and
family. Note that this doesn't imply that the assumption always hold true,
but it holds well enough to produce meaningful and useful
recommendations.
This assumption is the basis of simple recommendation engines and will
allowustogenerate arecommendation, butwhatisa
recommendation
?For
most, the first thought is some product, good, or service.
However, the recommendation can also be people if it the recommendation
engine is implemented on a social media or an Internet dating website, for
instance. The truth is that the recommendation can be anything because the
recommendation engine doesn't understand the concept of physical things,
which is why you will commonly see it referred to as simply an item.
Recommendation engines, for our purpose, use one of two paradigms:
collaborative filtering or clustering. Collaborative filtering is highly
dependent on both the assumption previously discussed and on historical
data.
This historical data records interaction or behavior with the items you are
attempting to generate recommendations for. Data that can be used is often
split into two distinct groups: explicit and implicit. Explicit data is
well-defined data such as purchase or click history. Implicit data, in
contrast, is more subjective and includes preferential or product ratings.
Table 12.2
provides a more complete list of examples for each category.
