<![CDATA[This research is motivated by the central role of recommendation algorithms in shaping the content consumption patterns of social media users, particularly on TikTok, which is experiencing rapid growth globally and locally. The research aims to analyze the influence of personal relevance, engagement rate, and frequency of exposure variables in TikTok's recommendation algorithm on content consumption behavior among users in Indonesia. An explanatory quantitative approach was used, with data collected through an online questionnaire involving 400 active TikTok users aged 18-35, which was then analyzed using multiple linear regression to test the relationships between variables. The results reveal that these three variables collectively contribute 68% to the variation in content consumption behavior, emphasizing the importance of recommendation algorithms in shaping user interaction with the platform. In conclusion, this research enriches the theoretical understanding of the influence of algorithms on digital behavior. It provides practical implications for platform developers and policymakers to enhance transparency and digital literacy to create a healthier and more diverse digital ecosystem.]]>
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