Quantifying the Potential to Escape Filter Bubbles: A Behavior-Aware Measure via Contrastive Simulation

Nowadays, recommendation systems have become crucial to online platforms, shaping user exposure by accurate preference modeling. However, such an exposure strategy can also reinforce users’ existing preferences, leading to a notorious phenomenon named filter bubbles. Given its negative effects, such as group polarization, increasing attention has been paid to exploring reasonable measures to filter bubbles. However, most existing evaluation metrics simply measure the diversity of user exposure, failing to distinguish between algorithmic preference modeling and actual information confinement. In view of this, we introduce Bubble Escape Potential (BEP), a behavior-aware measure that quantifies how easily users can escape from filter bubbles. Specifically, BEP leverages a contrastive simulation framework that assigns different behavioral tendencies (e.g., positive vs. negative) to synthetic users and compares the induced exposure patterns. This design enables decoupling the effect of filter bubbles and preference modeling, allowing for more precise diagnosis of bubble severity. We conduct extensive experiments across multiple recommendation models to examine the relationship between predictive accuracy and bubble escape potential across different groups. To the best of our knowledge, our empirical results are the first to quantitatively validate the dilemma between preferences modeling and filter bubbles. What's more, we observe a counter-intuitive phenomenon that mild random recommendations are ineffective in alleviating filter bubbles, which can offer a principled foundation for further work in this direction.

Authors

• Difu Feng Institute of Computing Technology, Chinese Academy of Sciences School of Computer Science and Technology, University of Chinese Academy of Sciences
• Qianqian Xu Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences
• Zitai Wang Institute of Computing Technology, Chinese Academy of Sciences
• Cong Hua Institute of Computing Technology, Chinese Academy of Sciences School of Computer Science and Technology, University of Chinese Academy of Sciences
• Zhiyong Yang School of Computer Science and Technology, University of Chinese Academy of Sciences
• Qingming Huang School of Computer Science and Technology, University of Chinese Academy of Sciences Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences Key Laboratory of Big Data Mining and Knowledge Management, University of Chinese Academy of Sciences

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