The Usage of internet in now a day is more and it became necessity for the people to do some applications such as searching web data bases in domains like Animation, vehicles, Movie, Real estates, etc. One of the problems in this context is ranking the results of a user query. Earlier approaches for addressing this problem have used frequencies of database values, query logs, and user profiles. A common thread in most of these approaches is that ranking is done in a user- and/or query-independent manner. This paper simulates the usage of ranking query results based on user and query Dependent ranks by taking user and query similarities as input including the workload. K- Means algorithm used for cluster and re ranking process, multiple database system used for clustering the data. Among rank learning methods, ranking SVM has been favorably applied to various applications, e. g. , optimizing search engines, improving data retrieval quality. We define these similarities formally in discuss their effectiveness analytically and experimentally over two distinct web databases.

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Automated Ranking Animation Database Vehicle And Movie Databases User Similarity Query Similarity Workload