Research on the Realization of Travel Recommendations for Different Users Through Deep Learning Under Global Information Management

2022 ◽  
Vol 30 (7) ◽  
pp. 0-0
Keyword(s):  
Deep Learning ◽  
Information Management ◽  
User Preference ◽  
Global Information ◽  
Tourist Destinations ◽  
The Past ◽  
Geographic Factor ◽  
Route Recommendation ◽  
Travel Route Recommendation ◽  
Travel Recommendation

This article is mainly to study the realization of travel recommendations for different users through deep learning under global information management. The personalized travel route recommendation is realized by establishing personalized travel dynamic interest (PTDR) algorithm and distributed lock manager (DLM) model. It is hoped that this model can provide more complete data information of tourist destinations on the basis of the past, and can also meet the needs of users. The innovation of this article is to compare and analyze with a large number of baseline algorithms, highlighting the superiority of this model in personalized travel recommendation. In addition, the model incorporates the topic factor features, geographic factor features, and user preference features to make the data more in line with user needs and improve the efficiency and applicability of the model. It is hoped that the plan proposed in this article can help users make choices of tourist destinations more conveniently.

scholarly journals Design and Development of a Real-Time Optimal Route Recommendation System Using Big Data for Tourists in Jeju Island

Electronics ◽  
2019 ◽  
Vol 8 (5) ◽  
pp. 506 ◽  
Author(s):  
Faisal Mehmood ◽  
Shabir Ahmad ◽  
DoHyeun Kim
Keyword(s):  
Big Data ◽  
Real Time ◽  
Recommendation System ◽  
Tourism Industry ◽  
Jeju Island ◽  
Third Party ◽  
User Preference ◽  
Optimal Route ◽  
Route Recommendation ◽  
Travel Route Recommendation

Nowadays researchers and engineers are trying to build travel route recommendation systems to guide tourists around the globe. The tourism industry is on the rise and it has attracted researchers to provide such systems for comfortable and convenient traveling. Mobile internet growth is increasing rapidly. Mobile data usage and traffic growth has increased interest in building mobile applications for tourists. This research paper aims to provide design and implementation of a travel route recommendation system based on user preference. Real-time big data is collected from Wi-Fi routers installed at more than 149 unique locations in Jeju Island, South Korea. This dataset includes tourist movement patterns collected from thousands of mobile tourists in the year 2016–2017. Data collection and analysis is necessary for a country to make public policies and development of the global travel and tourism industry. In this research paper we propose an optimal travel route recommendation system by performing statistical analysis of tourist movement patterns. Route recommendation is based on user preferences. User preference can vary over time and differ from one user to another. We have taken three main factors into consideration to the recommend optimal route i.e., time, distance, and popularity of location. Beside these factors, we have also considered weather and traffic condition using a third-party application program interfaces (APIs). We have classified regions into six major categories. Popularity of location can vary from season to season. We used a Naïve Bayes classifier to find the probability of tourists going to visit next location. Third-party APIs are used to find the longitude and latitude of the location. The Haversine formula is used to calculate the distance between unique locations. On the basis of these factors, we recommend the optimal route for tourists. The proposed system is highly responsive to mobile users. The results of this system show that the recommended route is convenient and allows tourists to visit maximum number of famous locations as compared to previous data.

Global Information Management Research

2012 ◽  
Vol 20 (1) ◽  
pp. 18-56 ◽  
Author(s):  
Fred Niederman ◽  
Hadi Alhorr ◽  
Yung-Hwal Park ◽  
Carri R. Tolmie
Keyword(s):  
Quantitative Analysis ◽  
Information Management ◽  
International Business ◽  
The Body ◽  
Future Research ◽  
Global Information ◽  
Management Research ◽  
Related Research ◽  
The Past ◽  
Research Findings

This study assesses the past decade in the GIM domain, based on Journal of Global Information Management (JGIM) research findings. Based on the issues addressed by these articles, the authors develop 11 topical categories and discuss each in terms of the accumulation of knowledge contributed by these findings. The authors also discuss for each topic possible extension and further understanding based on related research in international business. In consideration of the topics of these articles, a large number simultaneously addressing multiple topics and potential of explicitly linking these topics in future research are discussed. Additionally, the authors update prior quantitative analysis considering JGIM citations of key international business scholars, evolution of research methods, and levels of scope and analysis in these articles. Finally, the authors indicate gaps in the body of research within categories, when categories are considered in relationships, and when looking further from the perspective of recent IB research.

scholarly journals Discovering Travel Spatiotemporal Pattern Based on Sequential Events Similarity

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Juanjuan Chen ◽  
Liying Huang ◽  
Chengliang Wang ◽  
Nijia Zheng
Keyword(s):  
Sequence Similarity ◽  
Social Systems ◽  
Individual Preference ◽  
User Preference ◽  
Spatiotemporal Pattern ◽  
Novel Approach ◽  
Spreading Dynamics ◽  
Route Recommendation ◽  
New City ◽  
Travel Route Recommendation

Travel route preferences can strongly interact with the events that happened in networked traveling, and this coevolving phenomena are essential in providing theoretical foundations for travel route recommendation and predicting collective behaviour in social systems. While most literature puts the focus on route recommendation of individual scenic spots instead of city travel, we propose a novel approach named City Travel Route Recommendation based on Sequential Events Similarity (CTRR-SES) by applying the coevolving spreading dynamics of the city tour networks and mine the travel spatiotemporal patterns in the networks. First, we present the Event Sequence Similarity Measurement Method based on modelling tourists’ travel sequences. The method can help measure similarities in various city travel routes, which combine different scenic types, time slots, and relative locations. Second, by applying the user preference learning method based on scenic type, we learn from the user’s city travel historical data and compute the personalized travel preference. Finally, we verify our algorithm by collecting data of 54 city travellers of their historical spatiotemporal routes in the ten most popular cities from Mafeng.com. CTRR-SES shows better performance in predicting the user’s new city travel sequence fitting the user’s individual preference.

Sequence-Based Travel Route Recommendation Systems Using Deep Learning

2020 ◽  
Vol 9 (1) ◽  
pp. 45-50
Author(s):  
Hee Jun Lee ◽  
Won Sok Lee ◽  
In Hyeok Choi ◽  
Choong Kwon Lee
Keyword(s):  
Deep Learning ◽  
Recommendation Systems ◽  
Route Recommendation ◽  
Travel Route Recommendation

scholarly journals Travel Route Recommendation System using User Keyword Search

2020 ◽  
Vol 8 (6) ◽  
pp. 2052-2056
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Data Analytics ◽  
Recommendation System ◽  
Keyword Search ◽  
Customer Experience ◽  
Travel And Tourism ◽  
The Past ◽  
Route Recommendation ◽  
Travel Route Recommendation

Travel and tourism is a field, which have been growing substantially over the past few decades. The competitiveness in marketing and need of fulfilling customer experience in travel have given many opportunities for today’s technological advancements to play a crucial role in it. Those technology aspects are Big Data and Data Mining. Data Mining uses technologies of statistics, mathematics, machine learning and artificial intelligence. It aims to classify original, valid, useful, potentially and understand correlations and patterns. Data mining with the help of Big Data - Hadoop can help analyze and derive information, which can increase the growth of industry and give accurate suggestion to customer. The reason of combining capabilities of Hadoop is it can handle all sorts of data such as Structured or Unstructured. The main objective of this project also revolves around the same principle giving the best Customer Experience. By combining the power of Data Analytics of data mining, Big Data and programming capabilities of Java, this project focuses on building a customer centric Keyword Aware Travel Route Framework.”

Artificial Intelligence and Human Rights: Four Realms of Discussion: Summary of Remarks

2020 ◽  
Vol 114 ◽  
pp. 242-245
Author(s):  
Jootaek Lee
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Deep Learning ◽  
Language Processing ◽  
Human Reasoning ◽  
The Past ◽  
Making Choices ◽  
Data Capturing ◽  
Intensive Procedures

The term, Artificial Intelligence (AI), has changed since it was first coined by John MacCarthy in 1956. AI, believed to have been created with Kurt Gödel's unprovable computational statements in 1931, is now called deep learning or machine learning. AI is defined as a computer machine with the ability to make predictions about the future and solve complex tasks, using algorithms. The AI algorithms are enhanced and become effective with big data capturing the present and the past while still necessarily reflecting human biases into models and equations. AI is also capable of making choices like humans, mirroring human reasoning. AI can help robots to efficiently repeat the same labor intensive procedures in factories and can analyze historic and present data efficiently through deep learning, natural language processing, and anomaly detection. Thus, AI covers a spectrum of augmented intelligence relating to prediction, autonomous intelligence relating to decision making, automated intelligence for labor robots, and assisted intelligence for data analysis.

scholarly journals Deep-Emotion: Facial Expression Recognition Using Attentional Convolutional Network

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3046
Author(s):  
Shervin Minaee ◽  
Mehdi Minaei ◽  
Amirali Abdolrashidi
Keyword(s):  
Deep Learning ◽  
Facial Expression ◽  
Facial Expression Recognition ◽  
Expression Recognition ◽  
Visualization Technique ◽  
Convolutional Network ◽  
The Past ◽  
Multiple Datasets ◽  
The Face ◽  
Traditional Approaches

Facial expression recognition has been an active area of research over the past few decades, and it is still challenging due to the high intra-class variation. Traditional approaches for this problem rely on hand-crafted features such as SIFT, HOG, and LBP, followed by a classifier trained on a database of images or videos. Most of these works perform reasonably well on datasets of images captured in a controlled condition but fail to perform as well on more challenging datasets with more image variation and partial faces. In recent years, several works proposed an end-to-end framework for facial expression recognition using deep learning models. Despite the better performance of these works, there are still much room for improvement. In this work, we propose a deep learning approach based on attentional convolutional network that is able to focus on important parts of the face and achieves significant improvement over previous models on multiple datasets, including FER-2013, CK+, FERG, and JAFFE. We also use a visualization technique that is able to find important facial regions to detect different emotions based on the classifier’s output. Through experimental results, we show that different emotions are sensitive to different parts of the face.

scholarly journals Feature Extraction for Finger-Vein-Based Identity Recognition

2021 ◽  
Vol 7 (5) ◽  
pp. 89
Author(s):  
George K. Sidiropoulos ◽  
Polixeni Kiratsa ◽  
Petros Chatzipetrou ◽  
George A. Papakostas
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Extraction Methods ◽  
Identity Recognition ◽  
Learning Framework ◽  
Finger Vein ◽  
The Past ◽  
Biometric Systems ◽  
Vein Recognition ◽  
Finger Vein Recognition

This paper aims to provide a brief review of the feature extraction methods applied for finger vein recognition. The presented study is designed in a systematic way in order to bring light to the scientific interest for biometric systems based on finger vein biometric features. The analysis spans over a period of 13 years (from 2008 to 2020). The examined feature extraction algorithms are clustered into five categories and are presented in a qualitative manner by focusing mainly on the techniques applied to represent the features of the finger veins that uniquely prove a human’s identity. In addition, the case of non-handcrafted features learned in a deep learning framework is also examined. The conducted literature analysis revealed the increased interest in finger vein biometric systems as well as the high diversity of different feature extraction methods proposed over the past several years. However, last year this interest shifted to the application of Convolutional Neural Networks following the general trend of applying deep learning models in a range of disciplines. Finally, yet importantly, this work highlights the limitations of the existing feature extraction methods and describes the research actions needed to face the identified challenges.

Beyond Deep Learning: An Econometric Example

2020 ◽  
Vol 28 (Supp01) ◽  
pp. 31-38 ◽  
Author(s):  
Ruofan Liao ◽  
Paravee Maneejuk ◽  
Songsak Sriboonchitta
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Prediction Models ◽  
Original Data ◽  
Parametric Model ◽  
Parametric Models ◽  
The Past ◽  
Currency Exchange ◽  
Currency Exchange Rate ◽  
Linear And Nonlinear

In the past, in many areas, the best prediction models were linear and nonlinear parametric models. In the last decade, in many application areas, deep learning has shown to lead to more accurate predictions than the parametric models. Deep learning-based predictions are reasonably accurate, but not perfect. How can we achieve better accuracy? To achieve this objective, we propose to combine neural networks with parametric model: namely, to train neural networks not on the original data, but on the differences between the actual data and the predictions of the parametric model. On the example of predicting currency exchange rate, we show that this idea indeed leads to more accurate predictions.

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