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.

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.”

scholarly journals Towards building real-time, convenient route recommendation system for public transit

2016 ◽  
Author(s):  
Garvita Bajaj ◽  
Rachit Agarwal ◽  
Georgios Bouloukakis ◽  
Pushpendra Singh ◽  
Nikolaos Georgantas ◽  
...  
Keyword(s):  
Real Time ◽  
Recommendation System ◽  
Public Transit ◽  
Convenient Route ◽  
Route Recommendation

scholarly journals Exploring the Relationship between Perceived Big Data Advantages and Online Consumers’ Behavior: An Extended Hierarchy of Effects Model

2020 ◽  
Vol 13 (6) ◽  
pp. 73
Author(s):  
Jean-Luc Pradel Mathurin Augustin ◽  
Shu-Yi Liaw
Keyword(s):  
Big Data ◽  
Information And Communication Technologies ◽  
Customer Service ◽  
Information Search ◽  
Dynamic Pricing ◽  
Recommendation System ◽  
Tourism Industry ◽  
Social Awareness ◽  
Significance Level ◽  
Hierarchy Of Effects

This study intends to extend the hierarchy of effects model into the reality of the tourism industry after incorporation of information and communication technologies. Data analyses were conducted on 260 online questionnaires. The findings indicated consumer behavior follows a three-layer model: Attention-Intention/Desire-Action/Sharing-Social Awareness. Among big data advantages, recommendation system, information search and improved customer service are important to Attention-Intention; information search, dynamic pricing are important to Desire-Action with customer service (lower significance level); only customer service is important to Sharing-Social awareness. This model allows understanding of consumers’ behavior in online tourism as tourists are often sharing their experiences and raise awareness on service quality from e-vendors. Organizations might use big data to guarantee customers’ satisfaction and attract positive feedback particularly from the third layer of behavior.

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 Web-Based Recommendation System for Smart Tourism: Multiagent Technology

2019 ◽  
Vol 11 (2) ◽  
pp. 323 ◽  
Author(s):  
Raheleh Hassannia ◽  
Ali Vatankhah Barenji ◽  
Zhi Li ◽  
Habib Alipour
Keyword(s):  
Real Time ◽  
Web Application ◽  
Recommendation System ◽  
Online Communication ◽  
Data Communication ◽  
Tourism Industry ◽  
Time Data ◽  
Real Time Data ◽  
Hybrid Recommendation ◽  
Smart Tourism

The purpose of the study is to design and develop a recommended system based on agent and web technologies, which utilizes a hybrid recommendation filtering for the smart tourism industry. A hybrid recommendation system based on agent technology is designed by considering the online communication with other sectors in the tourism industry, such as the tourism supply chain, agency etc. However, online communication between the sectors via agents is designed and developed based on the contract net protocol. Furthermore, the design system is developed on the java agent development framework and implemented as a web application. Case study-based results considering two scenarios involving 100 customers illustrated that the proposed web application improves the rate of the recommendation for the customers. In the first scenario without disturbances, this rate was improved by 20% and the second scenario with disturbances yielded a 30% rate of acceptable recommendation. In addition, based on the second scenario, real time data communication on the system occurred, thus the proposed system supported real time data communication.

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