StreamProfileBench: A Benchmark for Fine-Grained User Profile Inference in Real-World Streaming Scenarios
arXiv:2605.25758v1 Announce Type: new Abstract: Large Language Models (LLMs) have reshaped user profiling, yet current evaluations mainly focus on static data snapshots. This paradigm overlooks the reality of personalized systems, where User-Generated Content (UGC) arrives continuously and fine-grained profile evolve rapidly. To bridge this gap, we introduce StreamProfileBench, a large-scale benchmark for fine-grained streaming user profiling. We formalize streaming user profiling as a continuous state maintenance task and curate a highly authentic dataset comprising over 120,000 UGC posts fro
The proliferation of User-Generated Content (UGC) and advancements in Large Language Models (LLMs) necessitate more dynamic and fine-grained user profiling, which static evaluation methods fail to address.
Accurate, real-time user profiling is critical for the next generation of personalized systems, advertising, and content moderation, directly impacting the effectiveness and value of AI applications.
The introduction of StreamProfileBench shifts the focus of user profiling evaluation from static snapshots to continuous, fine-grained updates in real-world streaming environments, reflecting the dynamic nature of user behavior.
- · AI agents developers
- · Personalized systems providers
- · Ad-tech companies
- · Content platforms
- · Companies relying solely on static user data
- · Legacy user profiling solutions
Improved accuracy and responsiveness of AI-powered personalized systems across various applications.
Enhanced user experience and engagement due to more relevant content and recommendations, leading to increased platform loyalty and monetization.
The development of highly sophisticated, self-evolving AI agents that can deeply understand and anticipate individual user needs and preferences in real-time.
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Read at arXiv cs.CL