Design of travel route recommendation system based on fast Spark artificial intelligence architecture

2021 ◽  
Vol 38 (3) ◽  
pp. 328
Author(s):  
Wei Xiaolu
Keyword(s):  
Artificial Intelligence ◽  
Recommendation System ◽  
Route Recommendation ◽  
Travel Route Recommendation

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 A Travel Route Recommendation System Based on Smart Phones and IoT Environment

2019 ◽  
Vol 2019 ◽  
pp. 1-16 ◽  
Author(s):  
Chenzhong Bin ◽  
Tianlong Gu ◽  
Yanpeng Sun ◽  
Liang Chang ◽  
Lei Sun
Keyword(s):  
Travel Behavior ◽  
Recommendation System ◽  
Behavior Pattern ◽  
Smart Phone ◽  
Vital Role ◽  
Travel Information ◽  
Tourist Behavior ◽  
Route Recommendation ◽  
Travel Route Recommendation ◽  
Mining Algorithms

Tourism recommendation systems play a vital role in providing useful travel information to tourists. However, existing systems rarely aim at recommending tangible itineraries for tourists within a specific POI due to their lack of onsite travel behavioral data and related route mining algorithms. To this end, a novel travel route recommendation system is proposed, which collects tourist onsite travel behavior data automatically regarding a specific POI based on smart phone and IoT technology. Then, the proposed system preprocesses the behavior data to transform raw behavior sequences into Tourist-Behavior pattern sequences. Subsequently, the system discovers frequent travel routes from the generated pattern sequences by using an original route mining algorithm, named Tourist-Behavior PrefixSpan. Finally, a route-recommending method is designed to search and rank tangible travel routes according to the querying tourist’s profile and constraint. The experimental results demonstrate that the proposed system is efficient and effective in recommending POI-oriented tangible travel routes considering tourists’ route constraints and personal profile while ensuring that the suggested routes have considerable route values.

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.”

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