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Journal of Intelligent Information Systems

Erschienen in:

01.04.2013

Tag recommendation by machine learning with textual and social features

verfasst von: Xian Chen, Hyoseop Shin

Erschienen in: Journal of Intelligent Information Systems | Ausgabe 2/2013

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Abstract

Tags are very popular in social media (like Youtube, Flickr) and provide valuable and crucial information for social media. But at the same time, there exist a great number of noisy tags, which lead to many studies on tag suggestion and recommendation for items including websites, photos, books, movies, and so on. The textual features of tags, likes tag frequency, have mostly been used in extracting tags that are related to items. In this paper, we address the problem of tag recommendation for social media users. This issue is as important as the tag recommendation for items, because the tags representing users are strongly related to the users’ favorite topics. We propose several novel features of tags for machine learning that we call social features as well as textual features. The experimental results of Flickr show that our proposed scheme achieves viable performance on tag recommendation for users.

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Favorite givers: We call the users who marked user u’s photos by favorite as favorite givers of user u.

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Titel: Tag recommendation by machine learning with textual and social features
verfasst von: Xian Chen
Hyoseop Shin
Publikationsdatum: 01.04.2013
Verlag: Springer US
Erschienen in: Journal of Intelligent Information Systems / Ausgabe 2/2013
Print ISSN: 0925-9902
Elektronische ISSN: 1573-7675
DOI: https://doi.org/10.1007/s10844-012-0200-0

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