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Query In Context (QIC) is a personalized search system that enhances individual search by incorporating user preferences in query expansion, capturing meanings embedded in documents, and ranking search results with context-enriched features. In this paper, we propose a new technique for QIC's Query Expansion module, which reformulates user queries by using novel statistical-based and knowledge-based query expansion techniques to improve the returned results. The promising preliminary results analyzed through precision and recall metrics show better alignment between the user's interests and the results retrieved.