The emergence of the content overloaded internet creates a lot of new challengesfor users and service providers a like. To minimize the displayed amount of contentlike movies, music, or other products service providers like Netflix or Amazonare using recommender systems which aim to guide the user trough the availableinformation. These systems collect knowledge about the user and try to deliver personalized experiences. Most of the state-of-the-art recommender systems are using acontent focused approach but often fail to grasp the nature of users’ desires. Therefore,a mood-as-input model is developed which combines the existing research onhuman mood identification and the emotion classification of content in the domainof movies. In order to match these two components different machine learning modelsare evaluated and a Random Forest is selected as the main matching algorithm.The results of this study indicate that the mood of a user can be used to create personalizedcontent recommendations and that it can perform better than an Arbitrarysystem.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:uu-353805 |
| Date | January 2018 |
| Creators | Wietreck, Niklas |
| Publisher | Uppsala universitet, Informationssystem |
| 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 |
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