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Ranking and classifying attractiveness of photos in folksonomies

Published: 20 April 2009 Publication History

Abstract

Web 2.0 applications like Flickr, YouTube, or Del.icio.us are increasingly popular online communities for creating, editing and sharing content. The growing size of these folksonomies poses new challenges in terms of search and data mining. In this paper we introduce a novel methodology for automatically ranking and classifying photos according to their attractiveness for folksonomy members. To this end, we exploit image features known for having significant effects on the visual quality perceived by humans (e.g. sharpness and colorfulness) as well as textual meta data, in what is a multi-modal approach. Using feedback and annotations available in the Web 2.0 photo sharing system Flickr, we assign relevance values to the photos and train classification and regression models based on these relevance assignments. With the resulting machine learning models we categorize and rank photos according to their attractiveness. Applications include enhanced ranking functions for search and recommender methods for attractive content. Large scale experiments on a collection of Flickr photos demonstrate the viability of our approach.

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cover image ACM Conferences
WWW '09: Proceedings of the 18th international conference on World wide web
April 2009
1280 pages
ISBN:9781605584874
DOI:10.1145/1526709

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 April 2009

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Author Tags

  1. attractiveness features
  2. classification
  3. folksonomy feedback
  4. image analysis
  5. photo appeal
  6. ranking
  7. web 2.0

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  • (2024)Shaping the Future of Content-based News Recommenders: Insights from Evaluating Feature-Specific Similarity MetricsProceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3627043.3659560(201-211)Online publication date: 22-Jun-2024
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