The internet is larger than ever and so is the amount of information on the internet.The average user on the internet has next to endless possibilities and choices whichcan cause information overload. Companies have therefore developed systems toguide their users to find the right product or object in the form of recommendersystems. Recommender systems are tools created to filter data and find patternsto recommend relevant information for specific customers with the help of differentalgorithms. MarketHype is a company that aggregates large amounts of data aboutevent organizers, their events, their visitors, and related transactions. They want inthe near future to be able to manage and offer event organizers recommended targetgroups for their events using a recommender system.This study tries to find a solution on how to model event data in a graph databaseto support relevant recommendations for event organizers. The method used to answer the question was an empirical research method. The goal was to create aprototype of a recommender system with help of event data. The main focus was tomodel a graph database in the software Neo4j that can be used for finding recommendations with different Cypher queries. A literature study was later conducted tofind what advantages and disadvantages a graph database could have on event data.This information could then answer how further development of the system couldwork.The result was a system that was implemented with the help of data from fourdifferent CSV files. The data provided were information about contacts, persons,orders, and events. This information was used to create the nodes and relationships.A total of 4.4 million nodes were created and around 5 million relationships betweenthose nodes. Collaborative and content-based filtering was the main recommendationtechnique used in order to find the best-suitable recommendations. This was donewith different queries in Cypher.The main conclusion is that a graph database in Neo4j is a good method in orderto implement a recommender system with event data. The result shows that thecollaborative filtering approach is a major factor in the system’s success in findingrelevant information. The approach of letting other contacts decide what the originalcontract wants is proven to work well with event data. The result also states thatthe recommendation is more of an indication because it returns what supposedlywould be the preferences for a contact. A solution for a better recommender systemwas found which includes another layer to the content-based filtering in the form ofcategorized events.