Database Reference
In-Depth Information
Questions and answers
Q1:Inordertobuildarecommendationsystem,Ineedanartiicialintelligence
engine that will take a look at my data and discover the recommendation patterns
for me automatically.
1.
True
2.
False
Answer:
False
. Recommender systems can be based on business knowledge that
your staff already have, a visual pattern you discover while browsing the data,
or some kind of algorithmic machine learning process. All three can provide
meaningful recommendation patterns for your business applications.
Q2: Recommender systems can only be applied in an Amazon-style retail
environment, where you have a massive amount of data to base your
recommendations on.
1.
True
2.
False
Answer:
False
. Recommendations are useful in many different business domains,
not just retail product recommendations. Fraud detection systems (I recommend
that you put this person in jail) are just one example of a business application that
has nothing to do with retail but that will use the same pattern matching capabilities
for detecting these more complicated fraud cases.
Summary
In this chapter, we gave you an overview of how graph databases such as Neo4j
could be used in a recommender system. There are a lot of things that we did not
discuss, which are out of the scope of this topic, but that would probably be part of
a true enterprise-class recommender system. Nevertheless, we hope to have illustrated
that the querying power of Neo4j will open up a wealth of new opportunities for
real-time recommender systems, where recommendations would no longer need to
be precalculated but rather leveraged in near real time.
The next chapter will use an example of a use case to teach you about analyzing
the impact change has on a process or system. It will also teach you how to
analyze impact through graphs.
