Published on Sep 03, 2023
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 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 images;
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 large-scale Flickr dataset demonstrate the effectiveness of the proposed method.
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The implementation stage involves careful planning, investigation of the existing system and it’s constraints on implementation, designing of methods to achieve changeover and evaluation of changeover methods.
1. USER-SPECIFIC TOPIC MODELING
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Processor - Pentium –III
Speed - 1.1 Ghz
RAM - 256 MB(min)
Hard Disk - 20 GB
Floppy Drive - 1.44 MB
Key Board - Standard Windows Keyboard
Mouse - Two or Three Button Mouse
Monitor - SVGA
Operating System :Windows95/98/2000/XP
Application Server : Tomcat5.0/6.X
Front End : HTML, Java, Jsp
Scripts : JavaScript.
Server side Script : Java Server Pages.
Database : Mysql
Database Connectivity : JDBC.
How to effectively utilize the rich user metadata in the social sharing websites for personalized search is challenging as well as significant. In this project we propose a novel framework to exploit the users’ social activities for personalized image search, such as annotations and the participation of interest groups. The query relevance and user preference are simultaneously integrated into the final rank list.
1) here, we only consider the simple case of one word-based queries. Actually, the construction of topic space provides a possible solution to handle the complex multiple words-based queries. We will leave it for our future work.
2) During the user-specific topic modeling process, the obtained user-specific topics represent the user’s distribution on the topic space and can be considered as user’s interest profile. Therefore, this framework can be extended to any applications based on interest profiles.
