<![CDATA[The development of Artificial Intelligence (AI)-based recommendation systems has enhanced the quality of digital services by providing recommendations tailored to user preferences. However, prior research has predominantly focused on algorithm development, fairness, and technical evaluation, leaving the integrated role of personalization and recommendation accuracy in building consumer trust relatively limited. This study aims to analyze the role of personalization and recommendation accuracy in building consumer trust toward AI-based recommendation systems. This research adopts a qualitative approach using the Systematic Literature Review (SLR) method on 15 scientific articles published between 2021 and 2025, retrieved from Google Scholar, Scopus, ScienceDirect, and Wiley Online Library databases. The results indicate that personalization enhances recommendation relevance according to user preferences, thereby strengthening perceived usefulness and trust in the system. Recommendation accuracy contributes to improving the quality of user decision-making by providing more precise and relevant recommendations. Furthermore, transparency and explainability reinforce the relationship between personalization, recommendation accuracy, and consumer trust by delivering easily understandable explanations regarding the rationale behind AI-generated recommendations. The findings suggest that personalization, recommendation accuracy, and transparency are complementary factors in building consumer trust toward AI-based recommendation systems.]]>
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