Wiki-MID Dataset (LOD + TSV)

Wiki-MID Dataset Wiki-MID is a LOD compliant multi-domain interests dataset to train and test Recommender Systems.
Our English dataset includes an average of 90 multi-domain preferences per user on music, books, movies, celebrities, sport, politics and much more, for about half million Twitter users traced during six months in 2017. Preferences are either extracted from messages of users who use Spotify, Goodreads and other similar content sharing platforms, or induced from their "topical" friends, i.e., followees representing an interest rather than a social relation between peers. In addition, preferred items are matched with Wikipedia articles describing them. This unique feature of our dataset provides a mean to categorize preferred items, exploiting available semantic resources linked to Wikipedia such as the Wikipedia Category Graph, DBpedia, BabelNet and others.
Data model:
Our resource is designed on top of the Semantically-Interlinked Online Communities (SIOC) core ontology. The SIOC ontology favors the inclusion of data mined from social networks communities into the Linked Open Data (LOD) cloud.We represent Twitter users as instances of the SIOC UserAccount class.Topical users and message based user interests are then associated, through the usage of the Simple Knowledge Organization System Namespace Document (SKOS) predicate relatedMatch, to a corresponding Wikipedia page as a result of our automated mapping methodology.