Dataset for aversion in intelligent recommendation algorithms

As intelligent recommendation algorithms become more widely used, they offer customers individualized services but also raise worries about potential threats during algorithm usage due to problems like information cocoons and privacy leaks. As a result, our study concentrates on the algorithmic technical aspect, uses the CAC theoretical model as the research framework, incorporates theories like self-efficacy and the FATE framework, and builds a chain mediation theoretical model of the influence mechanism of users' aversion behavior toward intelligent recommendation algorithms for analysis and research from the standpoint of user perception.