Personalized Image Search from the Photo Sharing Websites

Increasingly developed social sharing websites, like Flickr and You tube, allows users to create, share, annotates and comments 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. 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 modeling, it is necessary to enrich users’ annotation pool before user specific topic spaces construction. The proposed framework contains two components: 1) Ranking based Multi-correlation Tensor Factorization model is to perform annotation prediction 2) User-specific Topic Modeling to map the query relevance and user preference into the same user-specific topic space.