Abstract: [Purpose/Significance] While intelligent recommendation systems alleviate users' information overload, they also make users sense algorithm bias. Because the perception of Algorithmic bias reflect users' subjective feelings, exploring the influencing mechanism of algorithmic bias perception of intelligent recommendation users has an important implications for reducing the harm brought by algorithmic bias. [Design/Methodology] Utilizing grounded theory, this study explored the influencing mechanism of algorithmic bias perception of intelligent recommendation users.In the open coding phase, 175 initial concepts and 28 basic categories were identified. In the axial coding phase,10 principal categories were extracted. In the selective coding phase,"algorithmic bias perception" was identified as the core category. Finally, a theoretical model of influencing mechanism of algorithmic bias perception of intelligent recommendation users was developed. [Findings/Conclusion] The research results indicate that algorithmic literacy, personality traits, psychological state, recommendation narrowing, difference comparison, algorithm characteristics, and social environment directly affect users' perception of algorithmic bias. Furthermore, algorithmic characteristics, intelligent recommendation quality and social environment influence algorithmic bias perception through the mediation of psychological state. The impact of recommendation narrowing on the perception of algorithmic bias is moderated by algorithmic literacy. [Originality/Value] This study innovatively examines algorithmic bias based on user experience. The research findings provide references for both users to mitigate the impact of algorithmic bias and platforms to correct such algorithmic bias.

Keywords: Intelligent recommendation, Algorithmic bias perception, User experience, Influencing mechanism, Grounded Theory