Today, listening to podcasts is a common way of consuming media and it has been proven that listeners are much more recipient to advertisement when being addressed in a podcast, rather than through radio. This study has been performed at Acast, an audio-on-demand and podcast platform that hosts, monetizes, and distributes podcasts globally. With the use of machine learning, the goal of this study has been to obtain a credible estimate of how listeners outside the application tend to respond when exposed to ads in podcasts. The study includes a number of different machine learning models, such as Random Forest, Logistic Regression, Neural Networks and kNN. It was shown that machine learning could be applied to obtain a credible estimate of how ads are received outside the Acast application, based on data collected from the application. Additionally, out of the models included in the study, Random Forest was proven being the best performing model for this problem. Please note that the results presented in the report are based on a mix of real and simulated data.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:uu-390484 |
| Date | January 2019 |
| Creators | Hane, Sara, Angergård, Madeleine |
| Publisher | Uppsala universitet, Avdelningen för datalogi, Uppsala universitet, Avdelningen för datalogi |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | UPTEC STS, 1650-8319 ; 19016 |
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