Design and implementation of an intelligent recommendation system for tourist attractions: The integration of EBM model, Bayesian network and Google Maps

2012 ◽  
Vol 39 (3) ◽  
pp. 3257-3264 ◽  
Author(s):  
Fang-Ming Hsu ◽  
Yu-Tzeng Lin ◽  
Tu-Kuang Ho
Keyword(s):  
Bayesian Network ◽  
Recommendation System ◽  
Google Maps ◽  
Tourist Attractions ◽  
Design And Implementation

scholarly journals A Recommendation System Regarding Meeting Places for Groups during Events

2018 ◽  
Vol 7 (8) ◽  
pp. 296
Author(s):  
Shota Asukai ◽  
Kayoko Yamamoto
Keyword(s):  
Social Networking ◽  
Metropolitan Area ◽  
Questionnaire Survey ◽  
Recommendation System ◽  
Google Maps ◽  
Social Networking Services ◽  
Tokyo Metropolitan ◽  
Tokyo Metropolitan Area ◽  
Web Questionnaire ◽  
The Web

The present study aimed to design, develop, operate, and evaluate a recommendation system for meeting places targeting groups of two or more people during events. The system was designed and developed by integrating an accessibility database, as well as a recommendation system, and linking with Google Maps and social networking services (SNSs, Twitter and LINE). Additionally, the system was operated for 5 weeks with people mainly in the Tokyo metropolitan area, with Japan as the target, and the total number of users was 59. Based on the results of the web questionnaire survey, it was made evident that the system is useful for groups when meeting up, and the entry function for the nearest station to one’s home, as well as the recommendation function for meet-up stations, which was the original functions of the system, received generally good reviews. From the results of access analysis of the users’ log data, it was made evident that the system was used regardless of the type of device, just as the system was designed for, and that the system was used in harmony with the aim of the present study, which is to recommend meet-up stations for groups.

A qualitative attraction ranking model for personalized recommendations

2018 ◽  
Vol 9 (1) ◽  
pp. 2-13
Author(s):  
Thara Angskun ◽  
Jitimon Angskun
Keyword(s):  
Recommendation System ◽  
Theoretical Implication ◽  
Personalized Recommendation ◽  
The Novel ◽  
Analytic Hierarchy ◽  
Content Type ◽  
Tourist Attractions ◽  
Ranking Model ◽  
Travel Decisions ◽  
Hierarchy Process

Purpose This paper aims to find a way to personalize attraction recommendations for travelers. The research objective is to find a more accurate way to suggest new attractions to each traveler based on the opinions of other like-minded travelers and the traveler’s preferences. Design/methodology/approach To achieve the goal, developers have created a personalized system to generate attraction recommendations. The system considers an individual traveler’s preferences to construct a qualitative attraction ranking model. The new ranking model is the result of blending two processes: K-means clustering and the analytic hierarchy process (AHP). Findings The performance of the developed recommendation system has been assessed by measuring the accuracy and scalability of the ranking model of the system. The experimental results indicate that the ranking model always returns accurate results independent of the number of attractions and the number of travelers in each cluster. The ranking model has also proved to be scalable because the processing time is independent of the numbers of travelers. Additionally, the results reveal that the overall system usability is at a very satisfactory level. Research limitations/implications The main theoretical implication is that integrating the processes of K-means and AHP techniques enables a new qualitative ranking model for personalized recommendations that deliver only high-quality attractions. However, the designed recommendation system has some limitations. First, it is necessary to manually update information about the new tourist attractions. Second, the overall response time depends on the internet bandwidth and latency. Practical implications This research contributes to the tourism business and individual travelers by introducing an accurate and scalable way to suggest new attractions to each traveler. The potential benefit includes possible increased revenue for travel agencies that offer personalized package tours and support individual travelers to make the final travel decisions. The designed system could also integrate with itinerary planning systems to plot out a journey that pinpoints what travelers will most enjoy. Originality/value This research proposes a design and implementation of a personalized recommendation system based on the qualitative attraction ranking model introduced in this article. The novel ranking model is designed and developed by integrating K-means and AHP techniques, which has proved to be accurate and scalable.

Design and Implementation of the Web-Based Book Recommendation System

2013 ◽  
Vol 427-429 ◽  
pp. 2143-2146
Author(s):  
Qi Wang
Keyword(s):  
Recommendation System ◽  
Web Design ◽  
System Implementation ◽  
Systems Architecture ◽  
Web Based ◽  
Design And Implementation ◽  
Key Technologies ◽  
The Web

The book recommendation system is mainly designed with Struts2 + Hibernate + Spring technologies in the Web design. This paper analyzes the functions of the system, introduces the systems architecture and the key technologies of system implementation, and proves the effectiveness and practicability of the system through experiment.

Design and Implementation of E-Commerce Recommendation System Based on Ontology Technology

2014 ◽  
Vol 978 ◽  
pp. 244-247 ◽  
Author(s):  
Yi Wang ◽  
Hao Yuan Ou ◽  
Jian Ming Zhang
Keyword(s):  
Customer Satisfaction ◽  
Electronic Commerce ◽  
Customer Relationship Management ◽  
Recommender System ◽  
Recommendation System ◽  
Effective Means ◽  
Relationship Management ◽  
Customer Relationship ◽  
Semantic Structure ◽  
Design And Implementation

Electronic commerce recommendation system can effectively retain customers, effective means to improve the electronic commerce system sales. This paper first analyzes the E-commerce recommender system based on ontology, and applies it to the clothing e-commerce website customer relationship management and personalized commodity recommendation; semantic structure through ontology has to commodity recommendation. The paper presents design and implementation of E-commerce recommendation system based on ontology technology so as to effectively improve customer satisfaction.

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