The ultimate goal of a recommender system is to suggest interesting and not obvious items (e.g., products to buy, people to connect with, movies to watch, etc.) to users, based on their preferences.
The advent of the Linked Open Data (LOD) initiative in the Semantic Web gave birth to a variety of open knowledge bases freely accessible on the Web. They provide a valuable source of information that can improve conventional recommender systems, if properly exploited.
Here I present several approaches to recommender systems that leverage Linked Data knowledge bases such as DBpedia. In particular, content-based and hybrid recommendation algorithms will be discussed.
For full details about the presented approaches please refer to the full papers mentioned in this presentation.
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