The Influencing Factors of Privacy Information Disclosure in Personalized Recommendations of AI Human-Computer Interaction Users

In recent years, network platforms have become the norm through AI human-computer interaction to form user profiles for personalized recommendations,while exposing problems such as improper or excessive collection and use of personal information, and leakage of users' private information. The development of generative AI represented by ChatGPT has brought disruptive impact on human-computer interaction, as well as greater impact and challenges to data security and personal information protection. Therefore, there will be significant practical value and importance to the research on privacy information disclosure and personal information protection issues in the personalized recommendation of AI users of human-computer interaction. Through in-depth interviews with 15 AI human-computer interaction platform users and coding analysis with grounded theory method, the paper first defines the categories of sensitive and personal data that are disclosed when providing personalized recommendations to users of AI for human-computer interaction. It then examines users' awareness of privacy issues and the objective variables that influence their propensity to divulge such data. Finally, The paper first defines the categories of sensitive and personal data that are disclosed when providing personalized recommendations to users of AI for human-computer interaction. It then examines users' awareness of privacy issues and the objective variables that influence their propensity to divulge such data. The coding results provide four dimensions that highlight the contributing aspects of privacy information disclosure in AI human-computer interaction user tailored suggestion.: user factors, network platform factors, social environment factors and privacy calculus factors, so as to construct a theoretical analysis model and serve as an important guide to strengthen the regulation of privacy information disclosure risks and personal information protection in AI human-computer interaction user personalized recommendation.