Abstract: To address the problem of information narrowing caused by the bidirectional feedback characteristics of algorithmic recommendation systems which reveals its underlying mechanisms in the phenomenon of information cocoons in the digital age, a method of constructing a dynamic bidirectional feedback simulation model based on user algorithm is proposed. The model characterizes the positive feedback cycle mechanism of bidirectional feedback by quantifying user interest vector, openness parameter and confirmation bias strength, and combining the hybrid recommendation strategy of collaborative filtering and content filtering. The simulation results show that the degree of personalization of the algorithm, as the key driving factor, significantly affects the formation intensity and evolution rate of the information cocoon. The diversity of users’ initial interests constitutes the basic defense mechanism against the information cocoon. The higher the level of diversity, the lower the risk of individuals falling into the information cocoon. The user openness acts as the key postnatal adjustment variable of the information cocoon room, which regulates the development process of the information cocoon by affecting the reception and processing of heterogeneous information by users. The polarization degree of users’ final views is jointly determined by the personalized strength of the algorithm, the openness of users, and the diversity of users’ initial interests. The research systematically reveals the dynamic formation mechanism of information cocoon under the environment of recommendation algorithm, and provides empirical basis and theoretical support for algorithm governance.

Key words: information cocoon, recommendation algorithm, bidirectional feedback, formation mechanism