Nowadays, users can personalize the joint behavior of their connected entities, i.e., smart devices and online service, by means of trigger-action rules. As the number of supported technologies grows, however, so does the design space, i.e., the combinations between different triggers and actions: without proper support, users often experience difficulties in discovering rules and their related functionality. In this paper, we introduce TAPrec, an End-User Development platform that supports the composition of trigger-action rules with dynamic recommendations. By exploiting a hybrid and semantic recommendation algorithm, TAPrec suggests, at composition time, either a) new rules to be used or b) actions for auto-completing a rule. Recommendations, in particular, are computed to follow the user's high-level intention, i.e., by focusing on the rules' final purpose rather than on low-level details like manufacturers and brands. We compared TAPrec with a widely used trigger-action programming platform in a study on 14 end users. Results show evidence that TAPrec is appreciated and can effectively simplify the personalization of connected entities: recommendations promoted creativity by helping users personalize new functionality that are not easily noticeable in existing platforms.

TAPrec: Supporting the Composition of Trigger-Action Rules Through Dynamic Recommendations / Corno, Fulvio; DE RUSSIS, Luigi; MONGE ROFFARELLO, Alberto. - STAMPA. - (2020), pp. 579-588. ((Intervento presentato al convegno IUI '20: ACM International Conference on Intelligent User Interfaces tenutosi a Cagliari (Italy) nel 17-20 March, 2020 [10.1145/3377325.3377499].

TAPrec: Supporting the Composition of Trigger-Action Rules Through Dynamic Recommendations

Fulvio Corno;Luigi De Russis;Alberto Monge Roffarello
2020

Abstract

Nowadays, users can personalize the joint behavior of their connected entities, i.e., smart devices and online service, by means of trigger-action rules. As the number of supported technologies grows, however, so does the design space, i.e., the combinations between different triggers and actions: without proper support, users often experience difficulties in discovering rules and their related functionality. In this paper, we introduce TAPrec, an End-User Development platform that supports the composition of trigger-action rules with dynamic recommendations. By exploiting a hybrid and semantic recommendation algorithm, TAPrec suggests, at composition time, either a) new rules to be used or b) actions for auto-completing a rule. Recommendations, in particular, are computed to follow the user's high-level intention, i.e., by focusing on the rules' final purpose rather than on low-level details like manufacturers and brands. We compared TAPrec with a widely used trigger-action programming platform in a study on 14 end users. Results show evidence that TAPrec is appreciated and can effectively simplify the personalization of connected entities: recommendations promoted creativity by helping users personalize new functionality that are not easily noticeable in existing platforms.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2779432