Recommendation engines
Many brick-and-mortar and online retailers collect data about their customers' shopping habits. However, many of them fail to properly utilize this data to their advantage. Graph databases, such as Neo4j, can help assemble the bigger picture of customer habits for searching and purchasing, and even take trends in geographic areas into consideration.
For example, purchasing data may contain patterns indicating that certain customers tend to buy certain beverages on Friday evenings. Based on the relationships of other customers to products in that area, the engine could also suggest things such as cups, mugs, or glassware. Is the customer also a male in his thirties from a sports-obsessed area? Perhaps suggesting a mug ...
Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.
Read now
Unlock full access