Recommender systems have been around for decades to help people find the best matching item in a pre-defined item set. Knowledge-based recommender systems are used to match users based on information that links the two, but they often focus on a single, specific application, such as movies to watch or music to listen to. In this presentation, we present our Interest-Based Recommender System (IBRS). This knowledge-based recommender system provides recommendations that are generic in three dimensions: IBRS is (1) domain-independent, (2) language-independent, and (3) independent of the used social medium. To match user interests with items, the first are derived from the user's social media profile, enriched with a deeper semantic embedding obtained from the generic knowledge base DBpedia. These interests are used to extract personalized recommendations from a tagged item set from any domain, in any language. We also present the results of a validation of IBRS by a test user group of 44 people using two item sets from separate domains: greeting cards and holiday homes
