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

TL;DR

This paper reviews the current state of user modeling in Model-Driven Engineering, highlighting gaps in dynamic profiling, tool support, and proposing a unified, ML-based approach for automatic personalization of conversational agents.

Contribution

It provides a systematic literature review and advocates for a unified user model and ML-driven methods to enhance personalization in conversational agents.

Findings

Diverse proposals exist but are disconnected.

Most models are static rather than dynamic.

Limited tool support for user modeling.

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.