ChatGPT and Persuasive Technologies for the Management and Delivery of Personalized Recommendations in Hotel Hospitality

Recommender systems have become indispensable tools in the hotel hospitality industry, enabling personalized and tailored experiences for guests. Recent advancements in large language models (LLMs), such as ChatGPT, and persuasive technologies, have opened new avenues for enhancing the effectiveness of those systems. This paper explores the potential of integrating ChatGPT and persuasive technologies for automating and improving hotel hospitality recommender systems. First, we delve into the capabilities of ChatGPT, which can understand and generate human-like text, enabling more accurate and context-aware recommendations. We discuss the integration of ChatGPT into recommender systems, highlighting the ability to analyze user preferences, extract valuable insights from online reviews, and generate personalized recommendations based on guest profiles. Second, we investigate the role of persuasive technology in influencing user behavior and enhancing the persuasive impact of hotel recommendations. By incorporating persuasive techniques, such as social proof, scarcity and personalization, recommender systems can effectively influence user decision-making and encourage desired actions, such as booking a specific hotel or upgrading their room. To investigate the efficacy of ChatGPT and persuasive technologies, we present a pilot experi-ment with a case study involving a hotel recommender system. We aim to study the impact of integrating ChatGPT and persua-sive techniques on user engagement, satisfaction, and conversion rates. The preliminary results demonstrate the potential of these technologies in enhancing the overall guest experience and business performance. Overall, this paper contributes to the field of hotel hospitality by exploring the synergistic relationship between LLMs and persuasive technology in recommender systems, ultimately influencing guest satisfaction and hotel revenue.Comment: 17 pages, 12 figure

Recent advances in IoT, AI, and national technology resilience

Internet of Things (IoT) and Artificial Intelligence (AI) are the critical enablers of the Industrial Revolution 4.0. IoT can be used in many applications that require precision, such as agriculture, industrial automation, education, automotive, and smart cities, to name a few. In other words, IoT is a powerful technology that can solve various business problems. Nevertheless, its integration with AI can help to take automation to the next level. This talk aims to discuss the recent advances in IoT, edge computing, and its applications. First, the IoT and edge commercial adoption survey 2021 will be highlighted. Then, the IoT framework will be introduced to solve a complex problem, including Things, Connect, Collect, Learn, and Do. Especially, the Learn part is very much related to AI. Then, some applications using IoT and edge computing will be presented. Finally, national technology resilience is now a necessity rather than necessary due to the current world situation. Therefore, future directions to enhance national technology resilience will be elaborated

BMR: Benchmarking Metrics Recommender for personnel issues in software development proyects

This paper presents an architecture which applies document similarity measures to the documentation produced during the phases of software development in order to generate recommendations of process and people metrics for similar projects. The application makes a judgment of similarity of the Service Provision Offer (SPO) document of a new proposed project to a collection of Project History Documents (PHD), stored in a repository of unstructured texts. The process is carried out in three stages: firstly, clustering of the Offer document with the set of PHDs which are most similar to it; this provides the initial indication of whether similar previous projects exist, and signifies similarity. Secondly, determination of which PHD in the set is most comparable with the Offer document, based on various parameters: project effort, project duration (time), project resources (members/size of team), costs, and sector(s) involved, indicating comparability of projects. The comparable parameters are extracted using the GATE Natural Language Processing architecture. Lastly, a recommendation of metrics for the new project is made, which is based on the transferability of the metrics of the most similar and comparable PHD extracted, here referred to as recommendation.This work is supported by the Spanish Ministry of Industry, Tourism, and Commerce under the project SONAR (TSI-340000-2007-212), GODO2 (TSI- 020100-2008-564) and SONAR2 (TSI-020100-2008- 665) and the MID-CBR project of the Spanish Committee of Education & Science (TIN2006-15140- C03-02).Publicad

PROTOCOL: Effectiveness of interventions for improving social inclusion outcomes for people with disabilities in low‐ and middle‐income countries: A systematic review

The objectives of this review are to: (1) examine the effectiveness of interventions for improving social inclusion outcomes for people with disabilities (physical, visual, hearing, intellectual or mental health conditions) in low- and middle-income countries (LMICs); and (2) to critically appraise the confidence in study finding of the included studies. Key questions include: (1) Are interventions to improve social inclusion outcomes for people with disabilities in LMICs effective, and what is the quality of evidence base? (2) What types of intervention, or intervention design features, are most effective in improving social inclusion outcomes for people with disabilities in LMICs? (3) Which interventions appear most effective for different categories of disability? (4) What are the barriers to people with disabilities participating in interventions to improve their social inclusion outcomes? And what factors facilitate participation in, and the success of, such interventions?

RFID Technology in Intelligent Tracking Systems in Construction Waste Logistics Using Optimisation Techniques

Construction waste disposal is an urgent issue for protecting our environment. This paper proposes a waste management system and illustrates the work process using plasterboard waste as an example, which creates a hazardous gas when land filled with household waste, and for which the recycling rate is less than 10% in the UK. The proposed system integrates RFID technology, Rule-Based Reasoning, Ant Colony optimization and knowledge technology for auditing and tracking plasterboard waste, guiding the operation staff, arranging vehicles, schedule planning, and also provides evidence to verify its disposal. It h relies on RFID equipment for collecting logistical data and uses digital imaging equipment to give further evidence; the reasoning core in the third layer is responsible for generating schedules and route plans and guidance, and the last layer delivers the result to inform users. The paper firstly introduces the current plasterboard disposal situation and addresses the logistical problem that is now the main barrier to a higher recycling rate, followed by discussion of the proposed system in terms of both system level structure and process structure. And finally, an example scenario will be given to illustrate the system’s utilization