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The capability to easily find useful information on the Web becomes increasingly difficult as the available content increases. To assist users in finding relevant information Web search engines are developed. Those engines apply different strategies to measure the relevance of a Web page (its rank). However, page ranking is mainly conducted by relying on automatic assessment criteria. Hence, a gap is created between the effective relevance of a content and the computed one. To reduce this gap, we introduce a framework for feedback based Web search engine development. To illustrate the effectiveness and the use of the proposed framework, we developed a Web search engine prototype called social seeker.