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CNN-FM: Personalized Content-Aware Image Tag Recommendation

Nguyen, Hanh T. H.; Wistuba, Martin; Drumond, Rego Lucas; Schmidt-Thieme, Lars

Abstract:
Social media services allow users to share and annotate their resources freely with keywords or tags that have valuable information to support organizing or searching uploaded images or videos. Tag recommendation is used to encourage users to annotate their resources. Recommending tags of images to users not only depends on user preference but also strongly relies on the contents of images. In this paper, we propose a method for image tag recommendation using both image visual features and user past tagging behaviours by combining convolutional neural networks (CNN), which are widely used and have achieved high performance in image classification and recognition, and factorization machines (FM), since factorization models are the state-of-the-art approach for tag recommendation. Empirically, we demonstrate that learnable features extracted by CNNs can improve up to 7 percent the performance of FMs in image tag recommendation.


Zugehörige Institution(en) am KIT Institut für Informationswirtschaft und Marketing (IISM)
Publikationstyp Zeitschriftenaufsatz
Jahr 2017
Sprache Englisch
Identifikator DOI: 10.5445/KSP/1000058749/16
ISSN: 2363-9881
URN: urn:nbn:de:swb:90-687878
KITopen ID: 1000068787
Erschienen in Archives of Data Science Series A (Online First)
Band 2
Heft 1
Seiten 16 S. online
Lizenz CC BY-SA 4.0: Creative Commons Namensnennung – Weitergabe unter gleichen Bedingungen 4.0 International
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