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Semantic Search Log for Social Personalized Search

 

Personalization of web-based information systems based on specialized user models has become more important in order to preserve the effectiveness of their use as the amount of available content increases. In this system, we are proposing a novel technique called as Semantic Search log for Social Personalized Search. This novel technique is used to provide results for search query that relates to a particular user’s background, his area of interests, his likes and dislikes, the data he/she might have found to be useful for him while searching. In our system, supervised learning method is used for learning purpose. It is learn about the user based upon his interactions inside the system. User can give their basic information in their profile and get benefits from their each and every search. Inorder to obtain the semantics of videos; video extraction based on the fuzzy-ontology and rule based model is used in this project. When the user searches a keyword using the search engine inside the social network, according to the ontological profile of the user and displays the personalized search results. Our system can able to intelligently identify whether a search result has been useful to him or not and save it for his future reference when he searches for the same or similar keyword next time. From the experimental result, we obtain our system has high efficiency compared to other personalized search engine

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