TREC (Text Retrieval Conference) is a community evaluation workshop series. It aims to facilitate research in Information Retrieval through developing large-scale reusable test collections featured by human relevance assessments of retrieved documents. Each year, TREC is organized around a set of focus areas of IR called “tracks”. This year, the RALI lab participated in the Interactive Knowledge Acquisition track (iKAT). IKAT encourages research in Personalized Conversational Search by providing curated information-seeking dialogues alongside textual user profiles, challenging participants to design search engines that can effectively leverage both conversation history and user profiles to retrieve relevant documents from a large text corpus. In this presentation, we will go through our proposed solution for this track. Our approach achieves personalization via fusing ranking lists generated from personalized and non-personalized queries. Additionally, we will also discuss assessment bias we observed in the human relevance assessments process of iKAT.