This paper presents a multi-domain interests dataset to train and test Recommender Systems, and the methodology to create the dataset from Twitter messages in English and Italian. The English dataset includes an average of 90 preferences per user on music, books, movies, celebrities, sport, politics and much more, for about half million users. 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 derive a semantic categorization of the preferred items, exploiting available semantic resources linked to Wikipedia such as the Wikipedia Category Graph, DBpedia, BabelNet and others.

A large multilingual and multi-domain dataset for recommender systems / DI TOMMASO, Giorgia; Faralli, Stefano; Velardi, Paola. - ELETTRONICO. - (2018). ((Intervento presentato al convegno International Conference on Language Resources and Evaluation (LREC) tenutosi a Miyazaki, Japan.

A large multilingual and multi-domain dataset for recommender systems

Giorgia Di Tommaso
;
Stefano Faralli
;
Paola Velardi
2018

Abstract

This paper presents a multi-domain interests dataset to train and test Recommender Systems, and the methodology to create the dataset from Twitter messages in English and Italian. The English dataset includes an average of 90 preferences per user on music, books, movies, celebrities, sport, politics and much more, for about half million users. 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 derive a semantic categorization of the preferred items, exploiting available semantic resources linked to Wikipedia such as the Wikipedia Category Graph, DBpedia, BabelNet and others.
979-10-95546-00-9
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11573/1112827
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