Accurate Action Recommendation for Smart Home via Two-Level Encoders and Commonsense Knowledge

TL;DR

SmartSense is a novel smart home action recommendation system that leverages two-level encoders and commonsense knowledge to improve accuracy by effectively modeling context correlations and user intentions.

Contribution

It introduces a two-level encoding framework combined with commonsense knowledge transfer to enhance action recommendation accuracy in smart homes.

Findings

Achieves up to 9.8% higher mAP@1 than previous methods.

Effectively models context correlations and user intentions.

Addresses challenges of context handling and intention variability.

Abstract

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.