期刊名称：International Journal of Computer Science and Network Security
印刷版ISSN：1738-7906
出版年度：2014
卷号：14
期号：12
页码：34-39
出版社：International Journal of Computer Science and Network Security
摘要：Today��s web generates enormous data. Keyword based search is most popular medium to search content among web users. Metadata generated by web can be utilized to improve the search experience among the searchers. Social sharing websites like Flickr and Youtube allow users to create, share, tag, annotate, and comment Medias. The large amount of user-generated metadata can be effectively utilized for media retrieval and management. With Personalize search web search experience is improved by generating the results by considering user preferences and returned list accordingly. In this paper, we propose a model which simultaneously considers the user and query relevance to learn to personalized image search. In this essential work is to insert the user preference and query-related search intent into user-specific topic spaces. The given model is tested for double word query and showing satisfactory results
关键词：Metadata; Personalize search; RMTF; Social annotation; User preference s
