AI 中文总结
研究针对多数推荐系统局限单平台的问题，提出原生爪式推荐系统范式，介绍ClawRec系统，它能维护用户状态、组织检索并选候选者。通过ClawRec - SimBench基准测试，该系统性能超基线，提升了用户状态质量和时间对齐性。
AI 中文摘要
推荐系统已成为现代数字生态系统导航的重要组成部分。然而，大多数已部署的系统仍局限于单平台边界，观察局部交互痕迹并从孤立的候选空间中对项目进行排名。这种设计不太适合通过跨多个信息源的搜索、内容消费和比较来展开的现实世界任务。爪式个人代理能够持续访问授权的跨平台上下文，为围绕用户而非任何单个平台进行推荐创造了机会。本文引入了原生爪式推荐系统，这是一种超越平台本地排名的新范式，可生成跨越不同来源和内容形式的统一、互补推荐列表。为了实例化这一范式，我们展示了ClawRec，这是首个设计用于在此环境中原生运行的推荐系统。ClawRec维护一个证据关联、时间结构化的用户状态，将跨平台行为与跨源推荐联系起来。它围绕功能源角色组织检索，并根据候选者的边际效用进行选择，生成与用户当前任务一致的非冗余推荐列表。为了进行严格评估，我们引入了ClawRec - SimBench，这是一个由具体生活事件序列和跨平台行为轨迹构建的基准测试。实验表明，ClawRec优于最强基线，NDCG@20达到0.6134（提升0.1126），Hit@20达到0.6944（提升0.0854），同时还提高了用户状态质量和时间对齐性。我们的代码和数据集可在该https网址获取。
英文摘要
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.
