期刊名称：International Journal of Computer Science and Information Technologies
电子版ISSN：0975-9646
出版年度：2014
卷号：5
期号：4
页码：5720-5723
出版社：TechScience Publications
摘要：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. We believe user-generated metadata expresses user’s tastes and interests and can be used to personalize information to an individual user. Specifically, we describe a machine learning method that analyzes a corpus of tagged content to find hidden topics. We then these learned topics to select content that matches user’s interests. We 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. We use metadata associated with images tagged 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 that are of interest to her.
关键词：Social Annotation; Personalized Search; Tagging;Collaborative Filtering; Metadata; RMTF; User preference;User Specific topic; Query relevance
