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A Rule-Based Flickr Tag Recommendation System

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Book cover Social Media Retrieval

Part of the book series: Computer Communications and Networks ((CCN))

Abstract

Personalized tag recommendation focuses on helping users find desirable keywords (tags) to annotate Web resources based on both user profiles and main resource characteristics. Flickr is a popular online photo service whose resource sharing system significantly relies on annotations. However, recommending tags to a Flickr user who is annotating a photo is a challenging task as the lack of a controlled tag vocabulary makes the annotation history collection very sparse. This chapter presents a novel rule-based personalized tag recommendation system to suggest additional pertinent tags to partially annotated resources. Rules represent potentially valuable correlations among tag sets. Intuitively, the system should recommend tags highly correlated with the previously annotated tags. Unlike previous rule-based approaches, a Wordnet taxonomy is used to drive the rule mining process and discover rules, called generalized rules, that may contain either single tags or their semantically meaningful aggregations. The use of generalized rules in tag recommendation makes the system (1) more robust to data sparsity and (2) able to capture different viewpoints of the analyzed data. Experiments demonstrate the usefulness of generalized rules in recommending additional tags for real photos published on Flickr.

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Author information

Authors and Affiliations

  1. Politecnico di Torino. Corso Duca degli Abruzzi, 24 10129, Torino, Italy

    Luca Cagliero, Alessandro Fiori & Luigi Grimaudo

Authors
  1. Luca Cagliero
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  2. Alessandro Fiori
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  3. Luigi Grimaudo
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Corresponding author

Correspondence to Luca Cagliero .

Editor information

Editors and Affiliations

  1. Department of Electronic Engineering, Queen Mary University of London, Mile End Road, London, E1 4NS, United Kingdom

    Naeem Ramzan

  2. Director of Product Innovation, Search, Netflix, Winchester Circle 100, Los Gatos, 95032, California, USA

    Roelof van Zwol

  3. School of Integrated Technology, Yonsei University, 162-1 Songdo-dong, Yeonsu-gu, Incheon, 406-840, Korea, Republic of (South Korea)

    Jong-Seok Lee

  4. Institut für Telekommunikationssysteme, Technische Universität Berlin, Einsteinufer 17, Berlin, 10587, Germany

    Kai Clüver

  5. Media Computing Group, Microsoft Research, 555 108th Ave NE, Bellevue, 98004, Washington, USA

    Xian-Sheng Hua

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Cagliero, L., Fiori, A., Grimaudo, L. (2013). A Rule-Based Flickr Tag Recommendation System. In: Ramzan, N., van Zwol, R., Lee, JS., Clüver, K., Hua, XS. (eds) Social Media Retrieval. Computer Communications and Networks. Springer, London. https://doi.org/10.1007/978-1-4471-4555-4_8

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  • DOI: https://doi.org/10.1007/978-1-4471-4555-4_8

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