<![CDATA[The rapid advancement of Artificial Intelligence (AI) has transformed the e-commerce industry by enabling businesses to deliver personalized product recommendations that improve customer experiences and support data-driven marketing strategies. AI recommendation systems have become a fundamental component of digital retail because they analyze consumers' browsing histories, purchasing behaviors, and preferences to generate relevant product suggestions. This study aims to analyze the influence of AI recommendation systems on consumer purchasing decisions and examine the roles of personalization and consumer trust within the recommendation process. This research employed a quantitative explanatory research design using a cross-sectional survey of 312 e-commerce users who had previously purchased products based on AI-generated recommendations. Respondents were selected through purposive sampling, and data were collected using a structured questionnaire with a five-point Likert scale. The collected data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS software to evaluate both the measurement and structural models. The findings indicate that AI recommendation systems have a significant positive effect on consumer purchasing decisions. The results further demonstrate that personalized and trustworthy recommendations reduce consumers' information search effort, increase purchase confidence, and improve overall shopping experiences. In conclusion, AI recommendation systems enhance purchasing decisions when recommendations are perceived as relevant, personalized, accurate, and trustworthy. These findings contribute to the literature on AI-driven consumer behavior and provide practical guidance for e-commerce platforms and digital marketers seeking to improve recommendation strategies, strengthen consumer trust, and enhance customer engagement in increasingly competitive digital marketplaces.]]>
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