Extending singular value decomposition based recommendation systems with tags and ontology

Turgut, Yakup
Due to increase of the volume of data related to user ratings on items, in recent years, recommendation systems became very popular, which uses this data in order to rec- ommend items to users in many different domains. Singular Value Decomposition is one of the most widely studied collaborative filtering recommendation techniques. In some applications users are also allowed to enter (sometimes free) tags in addition to their ratings on items. Adding tags in addition to regular users’ ratings on items have also been studied from different perspectives. In this work, we embedded tags entered by users into SVD technique in a simple but novel way. We also present methods that incorporate ontology to determine relationships between tags into consideration while dealing with movie recommender systems. We have applied our approach on movie recommendation system.


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Recommendation systems suggest items to the user by estimating their preferences. Most of the recommendation systems are based on single criterion, such that they evaluate items based on overall rating. In order to give more accurate recommendations, a recommendation system can take advantage of considering multiple criteria. Beside combining multiple criteria from a single data source, multiple criteria from multiple data sources can be combined. Recommendation methods can also be used in various applicati...
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Citation Formats
Y. Turgut, “Extending singular value decomposition based recommendation systems with tags and ontology,” M.S. - Master of Science, Middle East Technical University, 2014.