| Summary: | This thesis investigates the possibility to create a machine learning powered recommendersystem from educational material supplied by a media provider company. By limiting theinvestigation to a single company's data the thesis provides insights into how a limited datasupply can be utilized in creating a first iteration recommender system. The methods includesemi structured interviews with system experts, constructing a model-building pipeline andtesting the models on system experts via a web interface. The study paints a good picture ofwhat kind of actions you can take when designing content based filtering recommender systemand what actions to take when moving on to further iterations. The study showed that userpreferences may be decisive for the relevancy of the provided recommendations for a specificmedia content. Furthermore, the study showed that Term Frequency Inverse DocumentFrequency modeling was significantly better than using an Elasticsearch database to serverecommendations. Testing also indicated that using term frequency document inversefrequency created a better model than using topic modeling techniques such as latent dirichletallocation. However as testing was only conducted on system experts in a controlledenvironment, further iterations of testing is necessary to statistically conclude that these modelswould lead to an increase in user experience.
|
|---|
