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Using Neo4j to build a recommendation engine based on collaborative filtering

We have see recently how to use a Neo4j database to run a recommendation engine for an online dating site (or for any recommendation problem). Today, we are going to see a different approach to that same problem based on collaborative filtering. What is collaborative filtering? Collaborative filtering is a technique used by recommendation engines. […]

Recommendation and graphs : an online dating use case

Graph technologies are very good for recommendation. It is no wonder that the biggest online dating websites are using it. We are going to see through a concrete example how to use graphs to find love with the Neo4j graph database. Online dating and graphs : a love story? In the last months a few […]

How to detect bank loan fraud with graphs : part 2

Last week we saw the sophisticated schemes criminals use to defraud banks. The TL;DR version of this is : criminals create fake identities, ask banks for loans and disappear with the money. By going through the techniques used by criminals we identified the graph challenge they face : creating and managing a network of identities […]

How to detect bank loan fraud with graphs : part 1

Everyday bank and insurance companies are victims of fraud. Criminal target them, open accounts, ask for loans and credit cards…and some day disappear. Eventually, banks have to write off the money loaned to fraudsters. It is estimated that in Canada alone, the cost of this fraud scheme is around $1B per year. We are going […]