The Relationship Between User Interest Diversity and Information Cocoon Formation Speed in Long-Term Recommendation Environments

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Personalized recommender systems may repeatedly expose users to similar content, potentially contributing to information cocoons. Existing research has mainly examined how recommendation algorithms influence content narrowing, while the role of persistent user characteristics remains less explored. This study investigates whether users with different initial levels of interest diversity experience different rates of information cocoon formation in a long-term recommendation environment. Using MovieLens 1M, interest diversity is measured with Shannon entropy over genre distributions. Users are classified into low-, medium-, and high-diversity groups, and 900 users are selected through stratified random sampling. A 20-round UserCF recommendation simulation is then conducted. Mean interest entropy decreases in all three groups, but at different rates. The high-diversity group exhibits the fastest decline, at approximately four times the rate of the low-diversity group. Contrary to the initial expectation, the low-diversity group declines most slowly. This may reflect a floor effect, because its initially concentrated interest distribution leaves less room for further reduction. These findings show that initial interest diversity is associated with the speed of information cocoon formation, shifting attention from whether cocoons form to how quickly they develop.