INFERENCE OF PRIVACY POLICY FOR USER-UPLOADED IMAGES ON CONTENT SHARING SOCIAL SITES

Increasingly developed social sharing websites, like Flickr and YouTube, allow users to create, share, annotate and comment Medias. 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. In this paper, we exploit the social annotations and propose a novel framework simultaneously considering the user and query relevance to learn to personalized image search. The basic premise is to embed the user preference and query-related search intent into user-specific topic spaces. Since the users’ original annotation is too sparse for topic modelling, we need to enrich users’ annotation pool before user specific topic spaces construction