Aaron Conrardy

Aaron Conrardy, Alfredo Capozucca, Jordi Cabot

In today's digital society, personalization has become a crucial aspect of software applications, significantly impacting user experience and engagement. A new wave of intelligent user interfaces, such as AI-based conversational agents, has the potential to enable such personalization beyond what other types of interfaces could offer in the past. Personalization requires the ability to specify a complete user profile, covering as many dimensions as possible, such as potential accessibility constraints, interaction preferences, and even hobbies. Yet, existing solutions for user modeling mostly focus on individual aspects at a very coarse level, severely limiting the potential adaptations for personalization. In this sense, this paper presents a unified user modeling language, aimed to combine previous approaches, both from the modeling community and other user-centric fields, in a single proposal. This language has been implemented on top of the open source BESSER low-code platform. Additionally, a proof of concept leveraging user profiles modeled with our language to automatically adapt a conversational agent has also been developed.

Aaron Conrardy, Alfredo Capozucca, Jordi Cabot

In this paper, we conducted an SLR on the state of user modeling in the MDE domain. Results show a diverse set of disconnected proposals, covering a partial number of dimensions with an emphasis on those characteristics that are easier to profile. Moreover, most dimensions are regarded as fixed instead of allowing their dynamic evolution during the interaction with the software application. It is also worth noting that tool support is also rather limited, mostly limited to enabling the creation of the user models itself. The roadmap we hope to see in this area stems from the discussion points seen above. For instance, we believe the community should agree on a unified and re-usable user model, covering the superset of all dimensions present in the literature. Plus additional ones we could learn from user profiling in other domains (e.g. sociology). On the technical side, we expect to see a new generation of ML-based proposals to automatically and incrementally derive a user profile from the analysis of user interactions and a number of automatic pipelines able to transform the user information in concrete application adaptations that personalize the application to cater to the user's needs and profile.

Aaron Conrardy, Armen Sulejmani, Cindy Guerlain et al.

The EU-funded Climaborough project supports European cities to achieve carbon neutrality by 2030. Eleven cities in nine countries will deploy in real conditions products and services fostering climate transition in their local environment. The Climaborough City Platform is being developed to monitor the cities' overall progress towards their climate goals by aggregating historic and real-time data and displaying the results in user-friendly dashboards that will be used by non-technical experts to evaluate the effectiveness of local experimental initiatives, identify those that yield significant impact, and assess the potential consequences of scaling them up to a broader level. In this paper, we explain how we have put in place a low-code/no-code strategy in Climaborough in response to the project's aim to quickly deploy climate dashboards. A low-code strategy is used to accelerate the development of the dashboards. The dashboards embed a no-code philosophy that enables all types of citizen profiles to configure and adapt the dashboard to their specific needs.