Use Case Example –Recommendations - Learning Neo4j - page 109

• A pattern application system : All recommender systems will be more or less
successful based on not just the patterns that they are able to discover, but
also based on the way that they are able to apply these patterns in business
applications. We can look at different types of applications for these patterns:
° Batch-oriented applications : Some applications of these patterns are
not as time-critical as one would expect. It does not really matter in
any material kind of way if a bulk e-mail with recommendations, or
worse, a printed voucher with recommended discounted products,
gets delivered to the customer or prospect at 10 am or 11 am. Batch
solutions can usually cope with these kinds of requests, even if they
do so in the most inefficient way.
° Real-time oriented applications : Some pattern applications simply
have to be delivered in real time, in between a web request and a web
response, and cannot be precalculated. For these types of systems,
which typically use more complex database queries in order to match
for the appropriate recommendation to make, graph databases such as
Neo4j are a fantastic tool to have. We will illustrate this going forward.
Withthisclassiicationbehindus,wewilllookatanexampledatasetandsome
example queries to make this topic come alive.
Using a graph model for recommendations
Wewillbeusingaveryspeciicdata model for our recommender system, which is
based on the dataset that we imported in the previous chapter. All we have changed
is that we added a couple of products and brands to the model, and inserted some
data into the database correspondingly. In total, we added the following:
• Ten products
• Three product brands
• Fifty relationships between existing person nodes and the mentioned
products, highlighting that these persons bought these products