Difference between revisions of "Wiki-Mid: a Very Large Multi-Domain Interests Dataset of Twitter Users with Mappings to Wikipedia"
(New work - Wiki-Mid: a Very Large Multi-Domain Interests Dataset of Twitter Users with Mappings to Wikipedia) |
(Links) |
||
| Line 1: | Line 1: | ||
| − | '''Wiki-Mid: a Very Large Multi-Domain Interests Dataset of Twitter Users with Mappings to Wikipedia''' - scientific work related to Wikipedia quality published in 2018, written by Giorgia Di Tommaso, Stefano Faralli, Giovanni Stilo and Paola Velardi. | + | '''Wiki-Mid: a Very Large Multi-Domain Interests Dataset of Twitter Users with Mappings to Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2018, written by [[Giorgia Di Tommaso]], [[Stefano Faralli]], [[Giovanni Stilo]] and [[Paola Velardi]]. |
== Overview == | == Overview == | ||
| − | This paper presents Wiki-MID, a LOD compliant multi-domain interests dataset to train and test Recommender Systems, and the methodology to create the dataset from Twitter messages in English and Italian. Authors 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 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. | + | This paper presents Wiki-MID, a LOD compliant multi-domain interests dataset to train and test Recommender Systems, and the methodology to create the dataset from [[Twitter]] messages in English and Italian. Authors 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 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. |
Revision as of 10:11, 2 August 2019
Wiki-Mid: a Very Large Multi-Domain Interests Dataset of Twitter Users with Mappings to Wikipedia - scientific work related to Wikipedia quality published in 2018, written by Giorgia Di Tommaso, Stefano Faralli, Giovanni Stilo and Paola Velardi.
Overview
This paper presents Wiki-MID, a LOD compliant multi-domain interests dataset to train and test Recommender Systems, and the methodology to create the dataset from Twitter messages in English and Italian. Authors 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 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.
