Problem
Research questions and friction points this paper is trying to address.
web browsing
behavioral fingerprinting
navigation prediction
user identification
next-domain prediction
Behavioral Fingerprinting and Navigation Prediction in Web Browsing
🤖 AI Summary
研究通过会话级用户识别和下个域名预测，使用经典模型、神经网络及大语言模型分析短期浏览活动的行为信号，揭示了交互历史对用户识别性和导航可预测性的贡献。
📝 Abstract
Web browsing often appears ephemeral: users visit a few websites, complete a task, and move on. However, even short fragments of browsing activity can contain rich and structured behavioral signals. In this work, we conduct a comparative empirical study of two complementary behavioral inference tasks: session-level user identification and next-domain prediction. Both tasks are derived from the same cleaned event stream and evaluated on large-scale anonymous browsing traces, with sessionization and splitting adapted to the temporal requirements of each task. For user identification, we evaluate classical and neural models operating on session-level behavioral and domain features. For next-domain prediction, we combine graph-based modeling with Large Language Models (LLMs). Experimental results show that short browsing sessions are highly identifiable, while future navigation actions are highly predictable from long-term interaction structure combined with recent behavioral context. Furthermore, LLM-derived semantic features yield only marginal gains over purely structural and sequential models, indicating that repeated interaction patterns remain the dominant predictive signal in the evaluated web-browsing setup. These findings highlight the extent to which interaction history substantially contributes to both user identifiability and navigation predictability in browsing traces.
Innovation
Methods, ideas, or system contributions that make the work stand out.
session-level behavioral and domain features
graph-based modeling
Large Language Models (LLMs)
navigation predictability
user identifiability
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