Nowadays, users can personalize Internet of Things (IoT) devices/web services via trigger-action programming (TAP). As the number of connected entities grows, the relations of triggers and actions become progressively complex (i.e., the heterogeneity of TAP), which becomes a challenge for existing models to completely preserve the heterogeneous data and semantic information in trigger and action. To address this issue, in this article, we propose IoT nonnegative matrix factorization (IoT-NMF), a NMF-based heterogeneous graph embedding method for TAP. Prior to using IoT-NMF, we map triggers and actions to an IoT heterogeneous information network, from which we can extract three structures that preserve heterogeneous relations in triggers and actions. IoT-NMF can factorize the structures simultaneously for getting low-dimensional representation vectors of the triggers and actions, which can be further utilized in Artificial Intelligence of Things applications (e.g., TAP rule recommendation). Finally, we demonstrate the proposed approach using an if this then that (IFTTT) dataset. The result shows that IoT-NMF outperforms the state-of-the-art approaches.

中文翻译：

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基于非负矩阵分解的物联网触发-动作编程异构图嵌入方法

如今，用户可以通过触发动作编程 (TAP) 来个性化物联网 (IoT) 设备/网络服务。随着连接实体数量的增加，触发器和动作的关系变得越来越复杂（即TAP的异构性），这成为现有模型在触发器和动作中完全保留异构数据和语义信息的挑战。为了解决这个问题，在本文中，我们提出了 IoT 非负矩阵分解 (IoT-NMF)，这是一种基于 NMF 的 TAP 异构图嵌入方法。在使用 IoT-NMF 之前，我们将触发器和动作映射到物联网异构信息网络，从中我们可以提取三种结构，以保留触发器和动作中的异构关系。IoT-NMF 可以同时分解结构以获得触发器和动作的低维表示向量，这可以进一步用于物联网应用（例如，TAP 规则推荐）。最后，我们使用 if this then that (IFTTT) 数据集演示了所提出的方法。结果表明，IoT-NMF 优于最先进的方法。