Learning to Recommend Trigger-Action Rules for End-User Development

Wu, Qinyue; Shen, Beijun; Chen, Yuting

doi:10.1007/978-3-030-64694-3_12

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Cited by 5 publications

References 18 publications

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Generating Scenario-Centric TAP Rules for Smart Homes by Mining Historical Event Logs

Liu,

Chen,

Wang

et al. 2023

2023 IEEE International Conference on Web Services (ICWS)

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Generating Scenario-Centric TAP Rules for Smart Homes by Mining Historical Event Logs

Liu,

Chen,

Wang

et al. 2023

2023 IEEE International Conference on Web Services (ICWS)

View full text Add to dashboard Cite

Proactive Detection of Physical Inter-rule Vulnerabilities in IoT Services Using a Deep Learning Approach

Huang,

Chen,

Lam

et al. 2024

2024 IEEE International Conference on Web Services (ICWS)

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Context-Aware Conversational Recommendation of Trigger-Action Rules in IoT Programming

Zhao

et al. 2021

Int. J. Soft. Eng. Knowl. Eng.

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Trigger-action (TA) programming is a programming paradigm that allows end-users to automate and connect IoT devices and online services using if-trigger-then-action rules. Early studies have demonstrated this paradigms usability, but more recent work has also highlighted complexities that arise in realistic scenarios. To facilitate end-users in TA programming, we propose AutoTAR, a context-aware conversational recommendation technique for recommending TA rules. AutoTAR leverages a TA knowledge graph to encode semantic features and abstract functionalities of rules, and then takes a two-phase method to recommend TA rules to end-users: during the context-aware recommendation phase, it elicits user preferences from programming context and recommends the top-N rules using a mixed content and collaborative technique; during the conversational recommendation phase, it justifies recommendations by iteratively raising questions and collecting feedback from end-users. We evaluate AutoTAR on Mturk and real data collected from the IFTTT community. The results show that our method outperforms state-of-the-arts significantly — its context-aware recommendation outperforms RecRules by 26% on R@5 and 21% on NDCG@5; its conversational recommendation outperforms LARecommender (a conversational recommender with the LA model) by 67.64% on accuracy. In addition, AutoTAR is effective in solving three problems frequently occurring in TA rule recommendations, i.e., the cold-start problem, the repeat-consumption problem, and the incomplete-intent problem.

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Learning to Recommend Trigger-Action Rules for End-User Development

Cited by 5 publications

References 18 publications

Generating Scenario-Centric TAP Rules for Smart Homes by Mining Historical Event Logs

Generating Scenario-Centric TAP Rules for Smart Homes by Mining Historical Event Logs

Proactive Detection of Physical Inter-rule Vulnerabilities in IoT Services Using a Deep Learning Approach

Context-Aware Conversational Recommendation of Trigger-Action Rules in IoT Programming

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