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Nowadays, end users can exploit end-user development (EUD) platforms to personalise their Internet of Things (IoT) ecosystems, typically through trigger–action rules. Unfortunately, within such platforms, users are forced to adopt a unique, vendor-centric abstraction: to define triggers and actions, they must specifically refer to every single device or online service needed to execute the intended behaviours. As a consequence, little social and practical benefits of EUD in this domain have emerged so far. In this paper, we build on the idea that other abstractions besides the vendor-centric one are possible, and that the growth of end-user personalisation in the IoT may depend on their identification. Specifically, we report on the results of a 1-week-long diary study during which 24 participants were free to collect trigger–action rules arising during their daily activities. First, we demonstrate that users would adopt different abstractions by personalising devices, information and people-related behaviours, where the individual is at the centre of the interaction. Then, we show that the adopted abstraction may depend on different factors, ranging from the user profile, e.g. their programming experience, to the context in which the personalisation is introduced. While users are inclined to personalise physical objects in the home, for example, they often go 'beyond devices' in the city, where they are more interested in the underlying information. Finally, we discuss the retrieved results by identifying new design opportunities to improve the relationship between users and the IoT. [ABSTRACT FROM AUTHOR]

In the Internet of Things era, users are willing to personalize the joint behavior of their connected entities, i.e., smart devices and online service, by means of trigger-action rules such as "IF the entrance Nest security camera detects a movement, THEN blink the Philips Hue lamp in the kitchen." Unfortunately, the spread of new supported technologies makes the number of possible combinations between triggers and actions continuously growing, thus motivating the need of assisting users in discovering new rules and functionality, e.g., through recommendation techniques. To this end, we present HeyTAP2, a semantic Conversational Search and Recommendation (CSR) system able to suggest pertinent IF-THEN rules that can be easily deployed in different contexts starting from an abstract user's need. By exploiting a conversational agent, the user can communicate her current personalization intention by specifying a set of functionality at a high level, e.g., to decrease the temperature of a room when she left it. Stemming from this input, HeyTAP2 implements a semantic recommendation process that takes into account (a) the current user's intention, (b) the connected entities owned by the user, and (c) the user's long-term preferences revealed by her profile. If not satisfied with the suggestions, then the user can converse with the system to provide further feedback, i.e., a short-term preference, thus allowing HeyTAP2 to provide refined recommendations that better align with the original intention. We evaluate HeyTAP2 by running different offline experiments with simulated users and real-world data. First, we test the recommendation process in different configurations, and we show that recommendation accuracy and similarity with target items increase as the interaction between the algorithm and the user proceeds. Then, we compare HeyTAP2 with other similar baseline recommender systems. Results are promising and demonstrate the effectiveness of HeyTAP2 in recommending IF-THEN rules that satisfy the current personalization intention of the user. [ABSTRACT FROM AUTHOR]

Highlights • Investigate whether a high-level representation can help end-user better create trigger-action rules in the IoT. • Present EUPOnt, a high-level semantic model for end-user development in the IoT. • Propose an end-user development platform to execute high-level rules. • Evaluate the suitability of the EUPont approach with a 30-people user study. Abstract Various programming environments for End-User Development (EUD) allow the composition of Internet of Things (IoT) applications, i.e., connections between IoT objects to personalize their joint behavior. These environments, however, only support a one-to-one mapping between pairs of object instances, and adopt a low level of abstraction that forces users to be aware of every single technology they may encounter in their applications. As a consequence, numerous open questions remain: would a "higher level" of abstraction help users creating their IoT applications more effectively and efficiently compared with the contemporary low-level representation? Which representation would users prefer? How high-level IoT applications could be actually executed? To answer these questions, we introduce EUPont, a high-level semantic model for EUD in the IoT. EUPont allows the creation of high-level IoT applications, able to adapt to different contextual situations. By integrating the ontology in the architecture of an EUD platform, we demonstrate how the semantic capabilities of the model allow the execution of high-level IoT applications. Furthermore, we evaluate the approach in a user study with 30 participants, by comparing a Web interface for composing IoT applications powered by EUPont with the one employed by a widely used EUD platform. Results show that the high-level approach is understandable, and it allows users to create IoT applications more correctly and quickly than contemporary solutions. [ABSTRACT FROM AUTHOR]