Do Generative Recommenders Deepen the Information Cocoon? A Closed-Loop Simulation with LLM-powered User Simulators

2026-06-16

AI Summary

This study investigates whether generative recommender systems exacerbate filter bubbles, particularly when incorporating semantic ID (SID) sequences compared to traditional approaches. To this end, the authors propose RecLoop, a large language model–based closed-loop simulation framework that models dynamic user–system interactions through iterative feedback loops, and introduce multidimensional evaluation metrics, including “code-space structural filter bubbles.” The findings reveal that generative recommenders exhibit weaker filter bubble effects at the exposure level than conventional sequential models, yet still show concentration tendencies in SID space. Moreover, collaborative-signal tokenization leads to stronger filter bubbles than semantic tokenization, while increasing model scale helps preserve diversity by retaining niche content.

Abstract

Recommender systems alleviate information overload, yet repeated feedback between recommendations and user interactions can reinforce existing preferences and narrow users' exposure, forming information cocoons. While this phenomenon has been widely studied in traditional sequential recommendation, its impact on generative recommendation remains unclear. By replacing atomic item IDs with Semantic ID (SID) sequences, generative recommenders introduce a different recommendation mechanism whose role in information cocoon formation is not yet understood. To investigate whether generative recommenders deepen information cocoons, we propose \textsc{RecLoop}, a closed-loop simulation framework with LLM-driven user agents. We compare two generative recommenders and two traditional sequential baselines on two Amazon datasets across multiple feedback cycles. In addition to standard exposure-level metrics, we introduce \emph{Code-Space Structural Cocoon}, a model-level metric that measures concentration in the generated SID space. Experimental results show that generative recommenders are generally less prone to exposure-level cocoon formation than traditional baselines, preserving broader exposure diversity and slowing cross-user homogenization. However, feedback loops can still induce concentration within the generated SID space. We further find that cocoon severity depends strongly on tokenization strategy and model scale: collaborative-signal tokenization produces stronger cocoon effects than semantic tokenization, whereas larger models maintain greater code-space diversity and better retain access to niche content. These findings suggest that information cocoons in generative recommendation are shaped not only by recommendation behavior, but also by item tokenization and model capacity. Our code is available at https://github.com/Dregen-Yor/RecLoop.

Problem

• generative recommenders
• information cocoon
• semantic ID
• feedback loop
• exposure diversity

Innovation

• Generative Recommenders
• Information Cocoon
• Semantic ID
• Closed-Loop Simulation
• LLM-powered User Simulators