How Algorithmic Domestication Shapes User Trust in Personalized Social Commerce Recommendations

With the deep integration of the Internet and artificial intelligence technologies, social commerce platforms have gradually become an important part of people’s digital lives. As a crucial bridge connecting users and platforms, personalized recommendation algorithms have significantly improved the efficiency of information distribution and user experience. However, while algorithm-driven recommendations bring convenience, they also intensify users’ concerns about platform trust mechanisms. Issues such as algorithmic opacity, information cocoons, and data privacy continue to emerge, resulting in complex user trust psychology towards recommended results. In recent years, algorithmic domestication theory has gradually become an important perspective for understanding user interactions with intelligent systems and the generation and evolution of trust. This paper reviews the development background of personalized recommendation technology on social commerce platforms, systematically expounds the connotation of algorithmic domestication and its manifestation in social commerce, analyzes the multidimensional structure of user trust and its main influencing factors, and discusses the shaping path of trust by algorithmic domestication. The research shows that algorithmic domestication plays a key role in cognitive, emotional, and behavioral trust and also places higher demands on platform design and management. Finally, the paper puts forward suggestions for improving platform algorithm transparency, enhancing user participation, and strengthening data security management, providing theoretical reference and practical enlightenment for the sustainable development of social commerce platforms.