Context-Aware Conversational Recommendation of Trigger-Action Rules in IoT Programming
| Autor: | Beijun Shen, Yuting Chen, Mingxin Zhao, Enze Ma, Qinyue Wu |
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| Rok vydání: | 2021 |
| Předmět: | |
| Zdroj: | International Journal of Software Engineering and Knowledge Engineering. 31:1517-1538 |
| ISSN: | 1793-6403 0218-1940 |
| DOI: | 10.1142/s0218194021500510 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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