[Purpose/Significance] The risks derived from algorithmic recommendation services have a significant impact on people's daily lives. Clarifying the generation mechanism of algorithmic recommendation service risks and the behavior characteristics of users' response to algorithmic recommendation service risks will help to further improve the algorithmic recommendation service system.[Method/Process] Based on the grounded theory and semi-structured interview, this study conducted in-depth one-on-one interviews with 30 Internet users. Combined with the protective action decision model and coping behavior theory, this study explored the users' coping mechanism, influence factors and decision paths of algorithmic recommendation service risks.[Result/Conclusion] The study finds that risk attributes, communication channels, platform characteristics and users' behavior characteristics will have an impact on the spread of algorithm recommendation service risks. Based on the cognition of risk communication, users will form the perception of risk, stakeholder and protective behavior. Then, users will form attitudes and effect evaluation on the basis of perception, and make decisions and adopt different coping behaviors based on this. Ultimately, this study puts forward suggestions from the three levels of algorithm literacy, risk participation and individual learning to deal with the risk of algorithm recommendation service.
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