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 modeling, we need to enrich users’ annotation pool before user specific topic spaces construction.
The proposed framework contains two components:
1) A Ranking based Multi-correlation Tensor Factorization model is proposed to perform annotation prediction, which is considered as users’ potential annotations for the mages;
2) We introduce User-specific Topic Modeling to map the query relevance and user preference into the same user-specific topic space. For performance evaluation, two resources involved with users’ social activities are employed. Experiments on a largescale Flickr dataset demonstrate the effectiveness of the proposed method.
In Existing System, Users may have different intentions for the same query, e.g., searching for “jaguar” by a car fan has a completely different meaning from searching by an animal specialist. One solution to address these problems is personalized search, where user-specific information is considered to distinguish the exact intentions of the user queries and re-rank the list results. Given the large and growing importance of search engines, personalized search has the potential to significantly improve searching experience.
In Proposed System We propose a novel personalized image search framework by simultaneously considering user and query information. The user’s preferences over images under certain query are estimated by how probable he/she assigns the query-related tags to the images.
1) A ranking based tensor factorization model named RMTF is proposed to predict users’ annotations to the images.
2) To better represent the query-tag relationship, we build user-specific topics and map the queries as well as the users’ preferences onto the learned topic spaces.
- User-Specific Topic Modeling
- Personalized Image Search
- Ranking – Multi Correlation based