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Internet has paved the way for the emergence of web databases. Querying such databases for required information has become a common task. Ranking such query results is an open problem to be addressed. The existing solutions such as user profiles, query logs, and database values perform ranking in user independent and/or query independent fashion. This can't provide efficient ranking. This paper presents a new approach known as Query and User Dependent Ranking for giving ranking to query results of deep web. The proposed ranking framework is based on two fundamental aspects to the problem of ranking query results. They are query similarity and user similarity. These similarities are exploited to make efficient ranking of query results. A prototype application is built to test the efficiency of our model. The empirical results revealed that our approach is efficient and can be used in real world applications. Index Terms: Deep web, ranking query results, user similarity, query similarity

Query- And User-Dependent Approach for Ranking Query Results in Web Databases