Perception research of college students under the background of algorithm recommendation

In the context of digital media development, algorithmic recommendation has become a key mechanism shaping how college students access and understand information. This article examines the influence of algorithmic recommendation on college students’ information perception from the perspective of communication and media studies. Rather than focusing only on macro-level consequences such as information cocoons and filter bubbles, the study emphasizes the intermediate level of perception, including how college students evaluate the completeness, credibility, importance, and diversity of information. Drawing on media dependency, affordance, and attention perspectives, the article argues that algorithmic recommendation does not directly determine cognition in a coercive way, but gradually restructures perceptual frameworks through personalized content distribution, repeated exposure, and attention guidance. Such influence is implicit, cumulative, and long-term, affecting not only the information college students receive, but also the way they interpret social reality and form judgments about public issues. The study highlights the importance of focusing on information perception in order to better understand the cognitive and social effects of algorithmic recommendation in the digital media environment.