Author(s): Naeem Naik, Prof. L. M. R. J. Lobo, Riyaz Jamadar
Published in: International Journal of Engineering Research & Technology
License: This work is licensed under a Creative Commons Attribution 4.0 International License.
Volume/Issue: Vol.1 - Issue 10 (December - 2012)
The present research aims at optimizing the image search time of websites for the users by using their personal data. The social media sites, such as Flicker and del.icio.us, allow users to upload content and annotate it with descriptive labels known as tags, join special-interest groups, etc. The large-scale user generated meta-data not only facilitate users in sharing and organizing multimedia content, but provide useful information to improve media retrieval and management. Personalized search serves as one of such examples where the web search experience is improved by generating the returned list according to the modified user search intents. User-generated metadata expresses user”Ēs tastes and interests and is used to personalize information to an individual user. Specifically, a machine learning method that analyzes a corpus of tagged content to find hidden topics. It then uses these learned topics to select content that matches a user”Ēs interests and it empirically validated this approach on the social photo-sharing site Flickr, which allows users to annotate images with freely selected tags and to search for images labeled with a particular tag. Metadata associated with images tagged is used with an ambiguous query term to identify topics corresponding to different senses of the term, and then personalize results of image search by displaying to the user only those images which are of interest to the user.
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