Supervised by

The web has become a real-time communication medium, used by a large amount of people, in ever-increasing parts of their daily life. This new us-age pattern gives advertisers and marketeers great opportunities to learn about their customers ’ temporal interests, thoughts and current context. Despite it’s a well known fact that this information is extremely valuable for advertising and product recommendation, online advertising is adapting only slowly. This is mainly due to the fact that it’s not clear what information is valuable to use, the huge amount of produced data and the lack of ecient models to process this data. This thesis describes an approach to implement Scalable Real-Time Product Recommendation based on Users Activity in a Social Network. The products are taken from Amazon.com and the used social network is the microblogging platform Twitter. It presents an implementation of this approach on top of the key-value database Cassandra, using a system called Triggy. Triggy extend