发表机构
Shopify; Columbia University（Shopify; 哥伦比亚大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
SimTrace提出一种基于真实用户轨迹和模拟网络环境的计算机使用代理框架，生成保真细粒度合成多模态点击流，在保真度和下游任务上优于基线，并开源发布。
AI 中文摘要
虚拟客户端为支持A/B测试、推荐系统开发和界面评估等应用提供了一种经济高效的方法。然而，构建虚拟客户端需要访问大规模、语义保真、细粒度的在线用户轨迹数据。由于专有日志受隐私限制，且小型企业往往缺乏足够的流量，这些数据难以获取。因此，现有的公开数据集要么抽象掉了细粒度的用户交互细节，要么保留了丰富的上下文但局限于特定平台且规模较小。为解决这一差距，我们提出了SimTrace，一个通过计算机使用客户端代理生成保真、细粒度的合成多模态点击流框架，该代理基于真实用户轨迹和给定的网络环境。SimTrace对真实交互进行匿名化处理，并构建给定网络环境的模拟孪生体，然后利用两者生成合成交互轨迹。每个动作都与其对应的网络观察和用户上下文配对，从而产生可共享的日志替代方案，用于开发计算机使用代理风格的虚拟客户端。我们将SimTrace应用于电子商务场景，并评估其保真度和下游实用性。SimTrace在8个保真度指标中的7个上优于竞争基线。在下游任务（如购买预测和推荐）中，基于合成数据训练的模型达到了与基于真实数据训练的模型相当的性能。对于下一个动作预测任务，将合成数据与真实数据相结合，相对于仅使用真实数据训练，准确率进一步提高了11.0%。我们以开源软件包的形式发布SimTrace，以促进在线用户行为建模的研究。
英文摘要
Virtual clients offer a cost-effective approach to support applications such as A/B testing, recommender system development, and interface evaluation. However, building them requires access to large-scale, semantically faithful, fine-grained online user trajectories. These data are difficult to obtain because proprietary logs are subject to privacy restrictions and small businesses often lack sufficient traffic. Consequently, existing public datasets either abstract away fine-grained user interaction details or preserve rich context but remain platform-specific and small-scale. To address this gap, we propose SimTrace, a framework that generates faithful, fine-grained synthetic multimodal clickstreams through a computer-use client agent that is grounded in real user trajectories and the given web environment. SimTrace anonymizes real interactions and constructs a simulated twin of the given web environment, then uses both to generate synthetic interaction trajectories. Each action is paired with its corresponding web observations and user context, yielding a shareable alternative to confidential logs for developing computer-use agent-style virtual clients. We apply SimTrace to an e-commerce setting and evaluate both its fidelity and downstream utility. SimTrace outperforms competing baselines on 7 out of 8 fidelity metrics. Models trained on synthetic data achieve performance comparable to those trained on real data on downstream tasks such as purchase prediction and recommendation. For next action prediction task, augmenting real data with synthetic data further improves accuracy by 11.0% relative to training on real data alone. We release SimTrace as an open-source package to facilitate research on online user behavior modeling.
