User Modeling in Model-Driven Engineering: A Systematic Literature Review

• URL: http://arxiv.org/abs/2412.15871v2
• Date: Thu, 15 May 2025 06:12:21 GMT
• Title: User Modeling in Model-Driven Engineering: A Systematic Literature Review
• Authors: Aaron Conrardy, Alfredo Capozucca, Jordi Cabot,
• Abstract summary: We conduct a systematic literature review to analyze existing proposals for user modeling in model-driven engineering (MDE) approaches.<n>The results showcase that there is a lack of a unified and complete user modeling perspective.<n>This limits the implementation of richer user interfaces able to better support the user-specific needs.
• Score: 1.7450893625541586
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In software applications, user models can be used to specify the profile of the typical users of the application, including personality traits, preferences, skills, etc. In theory, this would enable an adaptive application behavior that could lead to a better user experience. Nevertheless, user models do not seem to be part of standard modeling languages nor common in current model-driven engineering (MDE) approaches. In this paper, we conduct a systematic literature review to analyze existing proposals for user modeling in MDE and identify their limitations. The results showcase that there is a lack of a unified and complete user modeling perspective. Instead, we observe a lot of fragmented and partial proposals considering only simple user dimensions and with lack of proper tool support. This limits the implementation of richer user interfaces able to better support the user-specific needs. Therefore, we hope this analysis triggers a discussion on the importance of user models and their inclusion in MDE pipelines. Especially in a context where, thanks to the rise of AI techniques, personalization, based on a rich number of user dimensions, is becoming more and more of a possibility.

Related papers

• Towards a unified user modeling language for engineering human centered AI systems [1.7450893625541586]
  A new wave of intelligent user interfaces, such as AI-based conversational agents, has the potential to enable such personalization.<n>This paper presents the concepts of a unified user modeling language, aimed to combine previous approaches in a single proposal.<n>A proof of concept has been developed that leverages user profiles modeled using our language to automatically adapt a conversational agent.
  arXiv  Detail & Related papers  (2025-05-30T15:20:15Z)
• You Only Submit One Image to Find the Most Suitable Generative Model [48.67303250592189]
  We propose a novel setting called Generative Model Identification (GMI) GMI aims to enable the user to identify the most appropriate generative model(s) for the user's requirements efficiently.
  arXiv  Detail & Related papers  (2024-12-16T14:46:57Z)
• Personalized Multimodal Large Language Models: A Survey [127.9521218125761]
  Multimodal Large Language Models (MLLMs) have become increasingly important due to their state-of-the-art performance and ability to integrate multiple data modalities. This paper presents a comprehensive survey on personalized multimodal large language models, focusing on their architecture, training methods, and applications.
  arXiv  Detail & Related papers  (2024-12-03T03:59:03Z)
• Revisiting SMoE Language Models by Evaluating Inefficiencies with Task Specific Expert Pruning [78.72226641279863]
  Sparse Mixture of Expert (SMoE) models have emerged as a scalable alternative to dense models in language modeling. Our research explores task-specific model pruning to inform decisions about designing SMoE architectures. We introduce an adaptive task-aware pruning technique UNCURL to reduce the number of experts per MoE layer in an offline manner post-training.
  arXiv  Detail & Related papers  (2024-09-02T22:35:03Z)
• PEFT-U: Parameter-Efficient Fine-Tuning for User Personalization [9.594958534074074]
  We introduce the PEFT-U Benchmark: a new dataset for building and evaluating NLP models for user personalization. We explore the challenge of efficiently personalizing LLMs to accommodate user-specific preferences in the context of diverse user-centered tasks.
  arXiv  Detail & Related papers  (2024-07-25T14:36:18Z)
• Interlinking User Stories and GUI Prototyping: A Semi-Automatic LLM-based Approach [55.762798168494726]
  We present a novel Large Language Model (LLM)-based approach for validating the implementation of functional NL-based requirements in a graphical user interface (GUI) prototype. Our approach aims to detect functional user stories that are not implemented in a GUI prototype and provides recommendations for suitable GUI components directly implementing the requirements.
  arXiv  Detail & Related papers  (2024-06-12T11:59:26Z)
• Towards Unified Multi-Modal Personalization: Large Vision-Language Models for Generative Recommendation and Beyond [87.1712108247199]
  Our goal is to establish a Unified paradigm for Multi-modal Personalization systems (UniMP) We develop a generic and personalization generative framework, that can handle a wide range of personalized needs. Our methodology enhances the capabilities of foundational language models for personalized tasks.
  arXiv  Detail & Related papers  (2024-03-15T20:21:31Z)
• Earning Extra Performance from Restrictive Feedbacks [41.05874087063763]
  We set up a challenge named emphEarning eXtra PerformancE from restriCTive feEDdbacks (EXPECTED) to describe this form of model tuning problems. The goal of the model provider is to eventually deliver a satisfactory model to the local user(s) by utilizing the feedbacks. We propose to characterize the geometry of the model performance with regard to model parameters through exploring the parameters' distribution.
  arXiv  Detail & Related papers  (2023-04-28T13:16:54Z)
• Latent User Intent Modeling for Sequential Recommenders [92.66888409973495]
  Sequential recommender models learn to predict the next items a user is likely to interact with based on his/her interaction history on the platform. Most sequential recommenders however lack a higher-level understanding of user intents, which often drive user behaviors online. Intent modeling is thus critical for understanding users and optimizing long-term user experience.
  arXiv  Detail & Related papers  (2022-11-17T19:00:24Z)
• Modeling Proficiency with Implicit User Representations [18.4163404453651]
  Given a user's posts on a social media platform, the task is to identify the subset of posts or topics for which the user has some level of proficiency. This enables the filtering and ranking of social media posts on a given topic as per user proficiency. We investigate five alternative approaches to model proficiency, ranging from basic ones to an advanced, tailored user modeling approach.
  arXiv  Detail & Related papers  (2021-10-15T11:15:17Z)
• Modeling User Behaviour in Research Paper Recommendation System [8.980876474818153]
  A user intention model is proposed based on deep sequential topic analysis. The model predicts a user's intention in terms of the topic of interest. The proposed approach introduces a new road map to model a user activity suitable for the design of a research paper recommendation system.
  arXiv  Detail & Related papers  (2021-07-16T11:31:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.