论文标题
通过两级编码器和常识性知识为智能家居的准确行动建议
Accurate Action Recommendation for Smart Home via Two-Level Encoders and Commonsense Knowledge
论文作者
论文摘要
我们如何准确推荐用户在家中控制其设备的操作？智能家居的行动建议因其对虚拟助手和物联网（IoT）市场的潜在影响而引起了越来越多的关注。但是，为智能家居设计有效的动作推荐系统是一项挑战，因为它需要处理上下文相关性，考虑到用户的查询上下文和以前的历史，并处理历史上的反复无常意图。在这项工作中，我们提出了Smartsense，这是一种准确的智能家居推荐方法。对于个人动作，Smartsense以自我牵手的方式总结了其设备控制及其时间上下文，以反映它们之间相关性的重要性。 SmartSense然后总结了以查询方式考虑查询上下文的用户序列，以从顺序操作中提取与查询相关的模式。 Smartsense还将常识知识从常规数据转移到更好地处理动作序列中的意图。结果，Smartsense解决了针对Smart Home的所有三个主要挑战，并实现了比最佳竞争对手的最新性能高达9.8％的地图。
How can we accurately recommend actions for users to control their devices at home? Action recommendation for smart home has attracted increasing attention due to its potential impact on the markets of virtual assistants and Internet of Things (IoT). However, designing an effective action recommender system for smart home is challenging because it requires handling context correlations, considering both queried contexts and previous histories of users, and dealing with capricious intentions in history. In this work, we propose SmartSense, an accurate action recommendation method for smart home. For individual action, SmartSense summarizes its device control and its temporal contexts in a self-attentive manner, to reflect the importance of the correlation between them. SmartSense then summarizes sequences of users considering queried contexts in a query-attentive manner to extract the query-related patterns from the sequential actions. SmartSense also transfers the commonsense knowledge from routine data to better handle intentions in action sequences. As a result, SmartSense addresses all three main challenges of action recommendation for smart home, and achieves the state-of-the-art performance giving up to 9.8% higher mAP@1 than the best competitor.
