跨平台推荐新范式，让推荐系统围着用户转。
ClawRec: A Claw-Native Recommender System
- 基于跨平台行为构建动态用户状态，实现多源推荐融合。
- 在基准测试中NDCG@20达0.6134，优于基线0.1126。
- 适合需要多平台协同推荐的场景，如智能助手、数字生活管理。
推荐系统已成为现代数字生态的重要组成部分。然而，大多数已部署系统仍局限于单一平台，仅观察局部交互数据并从孤立候选池中排序。这种设计难以应对现实任务中跨信息源搜索、内容消费与比较的需求。具备持续跨平台上下文访问权限的爪形个人代理（Claw-style personal agents）为推荐系统围绕用户而非单一平台运作创造了机会。本文提出爪原生推荐系统（Claw-native recommender systems），一种突破平台局限的新范式，旨在生成跨越多样来源和内容形式的统一、互补推荐列表。为此，我们提出首个在此环境中原生运行的推荐系统ClawRec。ClawRec维护一个与证据关联、时间结构化的用户状态，将跨平台行为与跨源推荐相连接；其检索围绕功能源角色组织，依据边际效用选择候选项，生成与用户当前任务对齐的非冗余推荐列表。为支持严格评估，我们构建了ClawRec-SimBench，该基准基于具体生活事件序列与跨平台行为轨迹。实验表明，ClawRec超越最强基线，在NDCG@20上达到0.6134（+0.1126），Hit@20达0.6944（+0.0854），同时提升用户状态质量与时间对齐性。代码与数据集已公开于https://github.com/RUCAIBox/ClawRec。
原文摘要 · Abstract (English)
Recommender systems have become integral to navigating the modern digital ecosystem. Yet most deployed systems remain confined within single-platform boundaries, observing localized interaction traces and ranking items from isolated candidate spaces. This design is poorly suited to real-world tasks that unfold through searches, content consumption, and comparisons across multiple information sources. Claw-style personal agents, with persistent access to authorized cross-platform context, create an opportunity for recommendation to operate around the user rather than any single platform. In this paper, we introduce Claw-native recommender systems, a new paradigm that moves beyond platform-local ranking to produce unified, complementary recommendation slates spanning diverse sources and content forms. To instantiate this paradigm, we present ClawRec, the first recommender system designed to operate natively in this environment. ClawRec maintains an evidence-linked, temporally structured user state that connects cross-platform behaviors with cross-source recommendations. It organizes retrieval around functional source roles and selects candidates according to their marginal utility, producing non-redundant slates aligned with the user's active task. To enable rigorous evaluation, we introduce ClawRec-SimBench, a benchmark constructed from sequences of concrete life events and cross-platform behavior trajectories. Experiments show that ClawRec outperforms the strongest baselines, achieving an NDCG@20 of 0.6134 (+0.1126) and a Hit@20 of 0.6944 (+0.0854), while also improving user state quality and temporal alignment. Our code and dataset are available at https://github.com/RUCAIBox/ClawRec.
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