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Tag recommendation by machine learning with textual and social features

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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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Notes

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

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Acknowledgement

This paper was supported by Konkuk University in 2010.

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Authors and Affiliations

  1. Web Intelligence Laboratory, Konkuk University, Seoul, 143-701, South Korea

    Xian Chen & Hyoseop Shin

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  1. Xian Chen
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  2. Hyoseop Shin
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Correspondence to Hyoseop Shin.

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Chen, X., Shin, H. Tag recommendation by machine learning with textual and social features. J Intell Inf Syst 40, 261–282 (2013). https://doi.org/10.1007/s10844-012-0200-0

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