Abstract

This thesis investigates the possibility to create a machine learning powered recommender system from educational material provided by a media provider company. By limiting the investigation to a single company’s data the thesis provides insights into how a limited data supply can be utilized in creating a first iteration recommender system. The methods include semi structured interviews with system experts, constructing a model-building pipeline and testing the models on system experts via a web interface. The study paints a good picture of what kind of actions you can take when designing content based filtering recommender system and what actions to take when moving on to further iterations eventually creating a weighted ensamble recommender system that combines several filtering machine learning techniques.

The study showed that user preferences may be decisive for the relevancy of the provided recommendations for a specific media content. Furthermore the study showed that term frequency inverse document frequency modeling was significantly better than using an Elasticsearch engine to serve recommendations. Testing also indicated that using term frequency document inverse frequency created a better model than using topic modeling techniques such as latent dirichlet allocation. However as testing was only conducted on system experts in a controlled environment, further iterations of testing such as AB testing is necessary to statistically conclude that these models would lead to an increase in user experience.