  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.
