A Low-Code Approach for the Automatic Personalization of Conversational Agents

2026-05-04

AI Summary

This study addresses critical limitations in existing user modeling approaches within model-driven engineering, including fragmentation, incomplete dimensional coverage, and weak dynamic evolution capabilities, compounded by a lack of effective tool support. To overcome these challenges, the authors propose a low-code-driven unified user modeling framework that integrates multidimensional domain knowledge to construct reusable user models. Leveraging machine learning, the framework enables dynamic, incremental updates and automatic adaptation of user profiles. Grounded in a systematic literature review, user behavior analysis, and automated pipeline technologies, this work not only identifies current modeling shortcomings but also outlines a technical roadmap for developing dynamic, comprehensive, and automated personalized dialogue systems, thereby establishing a novel paradigm for efficient personalization in intelligent agents.

Abstract

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.

Problem

• user modeling
• personalization
• conversational agents
• model-driven engineering
• dynamic user profiles

Innovation

• Low-Code
• Conversational Agents
• User Modeling
• Automatic Personalization
• Machine Learning