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

arXiv: https://arxiv.org/abs/2208.06089 · HTML: https://mlsanity.com/paper/2208.06089/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 54 references that are in the map.
- First posted on arXiv 12 Aug 2022.

## Builds on (most cited first, 22 of 22)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Self-Attentive Sequential Recommendation](https://mlsanity.com/paper/1808.09781/) (2018)
- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer](https://mlsanity.com/paper/1904.06690/) (2019)
- [Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding](https://mlsanity.com/paper/1809.07426/) (2018)
- [A Survey on Deep Transfer Learning](https://mlsanity.com/paper/1808.01974/) (2018)
- [Session-based Recommendation with Graph Neural Networks](https://mlsanity.com/paper/1811.00855/) (2018)
- [Recurrent Neural Networks with Top-k Gains for Session-based Recommendations](https://mlsanity.com/paper/1706.03847/) (2017)
- [Fusing Similarity Models with Markov Chains for Sparse Sequential Recommendation](https://mlsanity.com/paper/1609.09152/) (2016)
- [Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks](https://mlsanity.com/paper/1708.04617/) (2017)
- [Sequential Recommender Systems: Challenges, Progress and Prospects](https://mlsanity.com/paper/2001.04830/) (2019)
- [Sequential Recommendation with Graph Neural Networks](https://mlsanity.com/paper/2106.14226/) (2021)
- [Translation-based Recommendation](https://mlsanity.com/paper/1707.02410/) (2017)
- [Memory Augmented Graph Neural Networks for Sequential Recommendation](https://mlsanity.com/paper/1912.11730/) (2019)
- [CauseRec: Counterfactual User Sequence Synthesis for Sequential Recommendation](https://mlsanity.com/paper/2109.05261/) (2021)
- [Context-aware Sequential Recommendation](https://mlsanity.com/paper/1609.05787/) (2016)
- [A Comparative Study of Matrix Factorization and Random Walk with Restart in Recommender Systems](https://mlsanity.com/paper/1708.09088/) (2017)
- [Data Context Adaptation for Accurate Recommendation with Additional Information](https://mlsanity.com/paper/1908.08469/) (2019)

## Built on by (most cited first, 8 of 8)

- [Make Your Home Safe: Time-aware Unsupervised User Behavior Anomaly Detection in Smart Homes via Loss-guided Mask](https://mlsanity.com/paper/2406.10928/) (2024)
- [Cold-start Bundle Recommendation via Popularity-based Coalescence and Curriculum Heating](https://mlsanity.com/paper/2310.03813/) (2023)
- [Diversely Regularized Matrix Factorization for Accurate and Aggregately Diversified Recommendation](https://mlsanity.com/paper/2211.01328/) (2022)
- [Calibration-Disentangled Learning and Relevance-Prioritized Reranking for Calibrated Sequential Recommendation](https://mlsanity.com/paper/2408.02156/) (2024)
- [Timing Matters: Enhancing User Experience through Temporal Prediction in Smart Homes](https://mlsanity.com/paper/2411.18719/) (2024)
- [ADLGen: Synthesizing Symbolic, Event-Triggered Sensor Sequences for Human Activity Modeling](https://mlsanity.com/paper/2505.17987/) (2025)
- [Semantic-aware Graph-guided Behavior Sequences Generation with Large Language Models for Smart Homes](https://mlsanity.com/paper/2508.03484/) (2025)
- [Wireless Power Transfer and Intent-Driven Network Optimization in AAVs-assisted IoT for 6G Sustainable Connectivity](https://mlsanity.com/paper/2511.18368/) (2025)

## Abstract (arXiv)

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.