| Summary: | Current search engine performances need to be improved because often the result suggested by search engine are determine the popularity of a given page for its associated keywords but does not match specific user expectations. Previous researches have indicated that only 20% to 45% of the common search results are relevant. The search becomes harder when the keyword used is a homographic word, and the results have caused user frustration. The work reported in this paper attempt to design Dominant User Context (DUC) Filtering Framework for personalized search in the effort to solve the problem. Literature analysis was performed to identify types of data commonly used for personalized search results. Using the results, this research developed a framework for search result cluster and dominant user contexts, and performed empirical study to validate the framework. The research performed two phases of survey in order to develop search result cluster and test the implementation of DUC. The result provides a novel idea to increase the relevance of search result and contribute to the body of knowledge in Distributed and Parallel Information Retrieval area. The result enables the enhancement of personalized search result by matching the user behavior, interest and ontology of metadata using the search keyword. This contributes to humanizing the search result, in which it will reduce the gap between human and computer. The result also contributes a new conceptual understanding of the types of data commonly used in developing the personalize search result. The developed DUC Filtering Framework provides a foundation in developing the algorithm for searching tools. This algorithm will largely benefit search engine as well as website search function in producing personalized search results. © 2012 IEEE.
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