Personalisation of content is a frequently used technique intended to improve user engagement and provide more value to users. Systems designed to provide recommendations to users are called recommender systems and are used in many different industries. This study evaluates the potential of personalisation in a media group primarily publishing local news, and studies how information stored by the group may be used for recommending content. Specifically, the study focuses primarily on content-based filtering by article tags and user grouping by demographics. This study first analyses the data stored by a media group to evaluate what information, data structures, and trends have potential use in recommender systems. These insights are then applied in the implementation of recommender systems, leveraging that data to perform personalised recommendations. When evaluating the performance of these recommender systems, it was found that tag-based content selection and demographic grouping each contribute to accurately recommending content, but that neither method is sufficient for providing fully accurate recommendations.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:liu-180329 |
| Date | January 2021 |
| Creators | Angström, Fredrik, Faber, Petra |
| Publisher | Linköpings universitet, Institutionen för datavetenskap |
| 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 |
Page generated in 0.0041 seconds