1,721,010 research outputs found
Integrating Human Feedback into a Reinforcement Learning-Based Framework for Adaptive User Interfaces
No full text[EN] Adaptive User Interfaces (AUI) play a crucial role in modern software applications by dynamically adjusting interface elements to accommodate users¿ diverse and evolving needs. However, existing adaptation strategies often lack real-time responsiveness. Reinforcement Learning (RL) has emerged as a promising approach for addressing complex, sequential adaptation challenges, enabling adaptive systems to learn optimal policies based on previous adaptation experiences. Although RL has been applied to AUIs,integrating RL agents effectively within user interactions remains a challenge.
In this paper, we enhance a RL-based Adaptive User Interface adaption framework by incorporating personalized human feedback directly into the leaning process. Unlike prior approaches that rely on a single pre-trained RL model, our approach trains a unique RL agent for each user, allowing individuals to actively shape their personal RL agent's policy, potentially leading to more personalized and responsive UI adaptations. To evaluate this approach, we conducted an empirical study to assess the impact of integrating human feedback into the RL-based Adaptive User Interface adaption framework and its effect on User Experience (UX). The study involved 33 participants interacting with AUIs incorporating human feedback and non-adaptive user interfaces in two domains: an e-learning platform and a trip-planning application.
The results suggest that incorporating human feedback into RL-driven adaptations significantly enhances UX, offering promising directions for advancing adaptive capabilities and user-centered design in AUIs.This work was funded by the GVA under the AKILA project (CIAICO/
2021/303) and by the AEI under the UCI-Adapt project (PID2022-
140106NB-I00). D. Gaspar-Figueiredo is funded by the GVA (ACIF/
2021/172), which is cofunded by the EU through the ESF.Gaspar-Figueiredo, D.;Fernández-Diego, Marta;Abrahao Gonzales, Silvia Mara;Insfran, Emilio (2025). Integrating Human Feedback into a Reinforcement Learning-Based Framework for Adaptive User Interfaces. En Association for Computing Machinery, EASE '25: Proceedings of the 29th International Conference on Evaluation and Assessment in Software Engineering (pp. 898-907). https://doi.org/10.1145/3756681.3757052S89890
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
La Movilidad Internacional como Actividad de Mejora y Renovación Docente
No full textInsfran, E. (2021). La Movilidad Internacional como Actividad de Mejora y Renovación Docente. Escola Tècnica Superior d'Enginyeria Informàtica. 94-97. https://riunet.upv.es/handle/10251/177198S949
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
A comparative study on reward models for user interface adaptation with reinforcement learning
Full text link[EN] Context
Adapting the User Interface (UI) of software systems to users¿ requirements and their context of use is a challenging task. It involves determining the right adaptation, at the right time and place, to make it valuable for end-users. We believe that recent progress in Machine Learning (ML) techniques could provide useful ways in which to support adaptation more effectively. In particular, Reinforcement Learning (RL) has proven to be effective in planning a sequence of UI adaptations over a long time horizon. However, RL requires either manually specifying a reward function or learning a reward model. Currently there is no empirical evidence supporting the usefulness of reward models for UI adaptation.
Objective
This paper presents a confirmatory empirical study aimed at investigating the effectiveness of two different approaches to generating reward models in the context of UI adaptation using reinforcement learning: (1) a reward model derived exclusively from predictive Human-Computer Interaction (HCI) models (AUI-HCI), and (2) a reward model derived from predictive HCI models augmented by human feedback (AUI-HCI-HF), compared to non-adaptive (NA) interfaces.
Method
A controlled experiment with an AB/BA crossover design was conducted to evaluate the impact of these reward models on user experience, measured through objective and subjective engagement, as well as user satisfaction. Our study contributes to the understanding of how reward modeling can facilitate UI adaptation through RL.
Results
The results showed a significant improvement in objective engagement for AUI-HCI-HF compared to non-adaptive interfaces. However, no significant differences were found between AUI-HCI and non-adaptive interfaces for any of the other measurements, across any conditions.
Conclusion
Integrating human feedback into RL reward models enhances objective engagement, but its impact on subjective engagement and user satisfaction remains limited. While AUI-HCI-HF shows promise for improving interaction metrics, further research is needed to better align reward models with broader user perceptions and preferences, particularly compared to non-adaptive interfaces.This work is supported by the AKILA project (CIAICO/2021/303) funded by the Generalitat
Valenciana (GVA) and the UCI-Adapt project (PID2022-140106NB-I00) funded by the Agencia Estatal de
Investigación (AEI). D. Gaspar-Figueiredo is funded by the GVA (ACIF/2021/172), which is co-funded by the European Union through the European Social Fund (ESF).Gaspar-Figueiredo, D.;Fernández-Diego, Marta;Abrahao Gonzales, Silvia Mara;Insfran, Emilio (2025). A comparative study on reward models for user interface adaptation with reinforcement learning. Empirical Software Engineering. 30(4):1-48. https://doi.org/10.1007/s10664-025-10659-5S14830
- …
