16,988 research outputs found
The News Delivery Channel Recommendation Based on Granular Neural Network
With the continuous maturation and expansion of neural network technology,
deep neural networks have been widely utilized as the fundamental building
blocks of deep learning in a variety of applications, including speech
recognition, machine translation, image processing, and the creation of
recommendation systems. Therefore, many real-world complex problems can be
solved by the deep learning techniques. As is known, traditional news
recommendation systems mostly employ techniques based on collaborative
filtering and deep learning, but the performance of these algorithms is
constrained by the sparsity of the data and the scalability of the approaches.
In this paper, we propose a recommendation model using granular neural network
model to recommend news to appropriate channels by analyzing the properties of
news. Specifically, a specified neural network serves as the foundation for the
granular neural network that the model is considered to be build. Different
information granularities are attributed to various types of news material, and
different information granularities are released between networks in various
ways. When processing data, granular output is created, which is compared to
the interval values pre-set on various platforms and used to quantify the
analysis's effectiveness. The analysis results could help the media to match
the proper news in depth, maximize the public attention of the news and the
utilization of media resources
Tourism mobile and recommendation systems - a state of the art
Recommendation systems have been growing in number for the last fifteen years. To evolve and adapt to the demands of the actual society, many paradigms emerged giving birth to even more paradigms and hybrid approaches. Mobile devices have also been under an incredible growth rate in every business area, and there are already lots of mobile based systems to assist tourists. This explosive growth gave birth to different mobile applications, each having their own advantages and disadvantages. Since recommendation and mobile systems might as well be integrated, this work intends to present the current state of the art in tourism mobile and recommendation systems, as well as to state their advantages and disadvantages
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
Considering temporal aspects in recommender systems: a survey
Under embargo until: 2023-07-04The widespread use of temporal aspects in user modeling indicates their importance, and their consideration showed to be highly effective in various domains related to user modeling, especially in recommender systems. Still, past and ongoing research, spread over several decades, provided multiple ad-hoc solutions, but no common understanding of the issue. There is no standardization and there is often little commonality in considering temporal aspects in different applications. This may ultimately lead to the problem that application developers define ad-hoc solutions for their problems at hand, sometimes missing or neglecting aspects that proved to be effective in similar cases. Therefore, a comprehensive survey of the consideration of temporal aspects in recommender systems is required. In this work, we provide an overview of various time-related aspects, categorize existing research, present a temporal abstraction and point to gaps that require future research. We anticipate this survey will become a reference point for researchers and practitioners alike when considering the potential application of temporal aspects in their personalized applications.acceptedVersio
Maintaining privacy for a recommender system diagnosis using blockchain and deep learning.
The healthcare sector has been revolutionized by Blockchain and AI technologies. Artificial intelligence uses algorithms, recommender systems, decision-making abilities, and big data to display a patient's health records using blockchain. Healthcare professionals can make use of Blockchain to display a patient's medical records with a secured medical diagnostic process. Traditionally, data owners have been hesitant to share medical and personal information due to concerns about privacy and trustworthiness. Using Blockchain technology, this paper presents an innovative model for integrating healthcare data sharing into a recommender diagnostic computer system. Using the model, medical records can be secured, controlled, authenticated, and kept confidential. In this paper, researchers propose a framework for using the Ethereum Blockchain and x-rays as a mechanism for access control, establishing hierarchical identities, and using pre-processing and deep learning to diagnose COVID-19. Along with solving the challenges associated with centralized access control systems, this mechanism also ensures data transparency and traceability, which will allow for efficient diagnosis and secure data sharing
A Review of Text Corpus-Based Tourism Big Data Mining
With the massive growth of the Internet, text data has become one of the main formats of tourism big data. As an effective expression means of tourists’ opinions, text mining of such data has big potential to inspire innovations for tourism practitioners. In the past decade, a variety of text mining techniques have been proposed and applied to tourism analysis to develop tourism value analysis models, build tourism recommendation systems, create tourist profiles, and make policies for supervising tourism markets. The successes of these techniques have been further boosted by the progress of natural language processing (NLP), machine learning, and deep learning. With the understanding of the complexity due to this diverse set of techniques and tourism text data sources, this work attempts to provide a detailed and up-to-date review of text mining techniques that have been, or have the potential to be, applied to modern tourism big data analysis. We summarize and discuss different text representation strategies, text-based NLP techniques for topic extraction, text classification, sentiment analysis, and text clustering in the context of tourism text mining, and their applications in tourist profiling, destination image analysis, market demand, etc. Our work also provides guidelines for constructing new tourism big data applications and outlines promising research areas in this field for incoming years
A Review of Text Corpus-Based Tourism Big Data Mining
With the massive growth of the Internet, text data has become one of the main formats of tourism big data. As an effective expression means of tourists’ opinions, text mining of such data has big potential to inspire innovations for tourism practitioners. In the past decade, a variety of text mining techniques have been proposed and applied to tourism analysis to develop tourism value analysis models, build tourism recommendation systems, create tourist profiles, and make policies for supervising tourism markets. The successes of these techniques have been further boosted by the progress of natural language processing (NLP), machine learning, and deep learning. With the understanding of the complexity due to this diverse set of techniques and tourism text data sources, this work attempts to provide a detailed and up-to-date review of text mining techniques that have been, or have the potential to be, applied to modern tourism big data analysis. We summarize and discuss different text representation strategies, text-based NLP techniques for topic extraction, text classification, sentiment analysis, and text clustering in the context of tourism text mining, and their applications in tourist profiling, destination image analysis, market demand, etc. Our work also provides guidelines for constructing new tourism big data applications and outlines promising research areas in this field for incoming years
Tourist experiences recommender system based on emotion recognition with wearable data
The collection of physiological data from people has been facilitated due to the mass use of cheap wearable devices. Although the accuracy is low compared to specialized healthcare devices, these can be widely applied in other contexts. This study proposes the architecture for a tourist experiences recommender system (TERS) based on the user’s emotional states who wear these devices. The issue lies in detecting emotion from Heart Rate (HR) measurements obtained from these wearables. Unlike most state-of-the-art studies, which have elicited emotions in controlled experiments and with high-accuracy sensors, this research’s challenge consisted of emotion recognition (ER) in the daily life context of users based on the gathering of HR data. Furthermore, an objective was to generate the tourist recommendation considering the emotional state of the device wearer. The method used comprises three main phases: The first was the collection of HR measurements and labeling emotions through mobile applications. The second was emotional detection using deep learning algorithms. The final phase was the design and validation of the TERS-ER. In this way, a dataset of HR measurements labeled with emotions was obtained as results. Among the different algorithms tested for ER, the hybrid model of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks had promising results. Moreover, concerning TERS, Collaborative Filtering (CF) using CNN showed better performance.This research was financially supported by the Ministry of Science, Technology,
and Innovation of Colombia (733-2015) and by the Universidad Santo Tomás Seccional Tunja. We
thank the members of the GICAC group (Research Group in Administrative and Accounting Sciences)
of the Universidad Santo Tomás Seccional Tunja for their participation in the experimental phase of
this investigation
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