In the thesis we compare several models for prediction of user preferences. The focus is mainly on Content Based models which work with metadata about objects that are recommended. These models are compared with other models which do not use metadata for recommendation. We use three datasets and three metrics to get the results of recommendation. The goal of the thesis is to find out how can the metadata about the users and the objects enhance the standard recommender models. However, the result is that the metadata can enhance recommendation in some cases, but it varies by used metrics and dataset. This enhancement is not significant.
| Identifer | oai:union.ndltd.org:nusl.cz/oai:invenio.nusl.cz:345248 |
| Date | January 2016 |
| Creators | Ferenc, Matej |
| Contributors | Vojtáš, Peter, Peška, Ladislav |
| Source Sets | Czech ETDs |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/masterThesis |
| Rights | info:eu-repo/semantics/restrictedAccess |
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