This paper presents Wiki-MID, a LOD compliant multidomain interests dataset to train and test Recommender Systems, and the methodology to create the dataset from Twitter messages in English and Italian. 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 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.
Di Tommaso, Giorgia; Faralli, Stefano; Stilo, Giovanni; Velardi, Paola. (2018). Wiki-MID: a very large Multi-domain Interests Dataset of Twitter users with mappings to Wikipedia. In The Semantic Web – ISWC 2018. 17th International Semantic Web Conference, Monterey, CA, USA, October 8–12, 2018, Proceedings, Part II (pp. 36- 52). Springer.
Wiki-MID: a very large Multi-domain Interests Dataset of Twitter users with mappings to Wikipedia
Giovanni Stilo;
2018
Abstract
This paper presents Wiki-MID, a LOD compliant multidomain interests dataset to train and test Recommender Systems, and the methodology to create the dataset from Twitter messages in English and Italian. 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 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.
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