In the recent years analysing shopping baskets has become quite appealing to retailers. For this recommendation system in super market terminology, think of users as shoppers and artists as items bought. Advanced technology made it possible for them to gather information on their customers and what they buy. The introduction of electronic point-in sale increased the use and application of transactional data in market basket analysis. In retail business analysing such information is highly useful for understanding buying behaviour. Identifying buying rules is crucial for every successful business. Transactional data is used for mining useful information on co-purchases and adjusting promotion and advertising accordingly. The well-known set of beer and diapers is just an example of an association rule found by data scientists.

The main objective of the thesis is to see how to select musical pieces that will likely be preferred by estimating user preferences and how to exploit these relations by marketing activities. Mining association rules from transactional data will provide us with valuable information about co-occurrences and co-purchases of artists.