Leveraging User-Specified Metadata to Personalize Image Search
The social media sites, such as Flickr and del.icio.us, allow users to upload content and annotate it with descriptive labels known as tags, join special-interest groups, etc. The authors believe user-generated metadata expresses user's tastes and interests and can be used to personalize information to an individual user. Specifically, they describe a machine learning method that analyzes a corpus of tagged content to find hidden topics. The authors then use these learned topics to select content that matches user's interests. They empirically validated this approach on the social photo-sharing site Flickr, which allows users to annotate images with freely chosen tags and to search for images labeled with a certain tag.