Abstract
With the consistent adoption of iVR and growing research on the topic, it becomes fundamental to understand how the perception of Realism plays a role in the potential of iVR. This work puts forwards a hypothesis-driven theoretical model of how the perception of each multisensory stimulus (Visual, Audio, Haptic and Scent) is related to the perception of Realism of the whole experience (Subjective Realism) and, in turn, how this Subjective Realism is related to Involvement and Presence. The model was validated using a sample of 216 subjects in a multisensory iVR experience. The results indicated a good model fit and provided evidence on how the perception of Realism of Visual, Audio and Scent individually is linked to Subjective Realism. Furthermore, the results demonstrate strong evidence that Subjective Realism is strongly associated with Involvement and Presence. These results put forwards a validated questionnaire for the perception of Realism of different aspects of the virtual experience and a robust theoretical model on the interconnections of these constructs. We provide empirical evidence that can be used to optimise iVR systems for Presence, Involvement and Subjective Realism, thereby enhancing the effectiveness of iVR experiences and opening new research avenues.

Abstract
Handheld controllers are standard in immersive virtual reality (iVR), but the rise of natural hand-based interactions exposes the limitations of hand gestures, especially for point-and-click tasks with graphical user interfaces (GUI). This shows the need to explore alternative hands-free selection methods. Unlike most studies focusing on the selection task itself, this work evaluates the impact of such methods on multiple dimensions when selections occur alongside another primary task. The tested methods were: head gaze + dwell, leaning, and voice; eye gaze + dwell, leaning, blinking, and voice; and voice-only. Controllers served as the baseline. Methods were further analyzed by pointing and confirming mechanisms. Four dimensions were analyzed: (1) iVR experience, (2) user satisfaction, (3) usability, and (4) efficiency and effectiveness. With 72 participants, results show hands-free methods provide comparable experiences to controllers, suggesting selection methods have a lower impact on the user experience when users focus on a primary task.

Abstract

Abstract
Traditional approaches in the competitive recruitment landscape frequently encounter difficulties in effectively identifying exceptional applicants, resulting in delays, increased expenses, and biases. This study proposes the utilisation of contemporary technologies such as Large Language Models (LLMs) and chatbots to automate the process of resume screening, thereby diminishing prejudices and enhancing communication between recruiters and candidates. Algorithms based on LLM can greatly transform the process of screening by improving both its speed and accuracy. By integrating chatbots, it becomes possible to have personalised interactions with candidates and streamline the process of scheduling interviews. This strategy accelerates the hiring process while maintaining principles of justice and ethics. Its objective is to improve algorithms and procedures to meet changing requirements and enhance the competitive advantage of talent acquisition within organisations.

Abstract
Recent advancements in deep learning and large language models (LLMs) have led to the development of innovative technologies that enhance recommender systems. Different heuristics, architectures, and techniques for filtering information have been proposed to obtain successful computational models for the recommendation problem; however, several issues must be addressed in online environments. This research focuses on a specific type of recommendation, which combines sequential recommendation with session-based recommendation. The goal is to solve the complex next-item recommendation problem in Web applications, using the wine domain as a case study. This paper describes a framework developed to provide adaptive recommendations by rethinking the initial data modeling to better understand users' dynamic taste profiles. Three main contributions are presented: (a) a novel dataset of wines called X-Wines; (b) an updated recommendation model named X-Model4Rec – eXtensible Model for Recommendation, which utilizes attention and transformer mechanisms central to LLMs; and (c) a collaborative Web platform designed to support adaptive wine recommendations for users in an online environment. The results indicate that the proposed framework can enhance recommendations in online environments and encourage further scientific exploration of this topic.   Received: 15 December 2024 | Revised: 12 June 2025 | Accepted: 30 June 2025   Conflicts of Interest The authors declare that they have no conflicts of interest to this work.   Data Availability Statement The data that support the findings of this study are openly available in X-Wines Research Project at https://sites.google.com/farroupilha.ifrs.edu.br/xwines.   Author Contribution Statement Rogério Xavier de Azambuja: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, and Project administration. A. Jorge Morais: Conceptualization, Methodology, Validation, Formal analysis, Data curation, Writing – review & editing, Visualization, Supervision, and Project administration. Vítor Filipe: Conceptualization, Methodology, Validation, Formal analysis, Data curation, Writing – review & editing, Visualization, and Project administration.

Abstract
Virtual Reality (VR) has been recognised as a promising technology for enhancing the tourist experience. However, little is known about the intention of tourism business managers to adopt VR for leisure purposes. In this context, this study aims to explore this intention by interviewing managers in the sector. This process allowed us to examine their perceptions regarding the use of this technology in their business models. The results revealed that the perceived usefulness of VR is a key factor in its adoption. In addition, managers recognise the value of VR as a complement to the tourist visit, and their intention to adopt it increases when a positive return on investment is anticipated. This approach offers a unique perspective on the main factors influencing technology adoption in this context, broadens the understanding of VR applications in wine tourism, and highlights its potential to transform the visitor experience and drive growth in the sector through innovative business models.