A NOVEL FRAMEWORK FOR IMAGE SEARCH IN SOCIAL NETWORKING

Most Internet users, you probably spend a decent amount of time using a search engine to find content and answers. Social network search engines are designed to do Search they can filter out all the unnecessary results we might get if we used a regular search engine. 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 propose a novel framework simultaneously considering the user and query relevance to learn to personalized image search. The proposed framework contains two components: one is a Ranking based model and another one is User-specific modeling to map the query relevance and user preference. We did experiments on large scale data to demonstrate the effectiveness of the proposed method. In this paper we consider the simple case of one word-based query. As well as topic based.