• Title/Summary/Keyword: tourism recommendation system

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Adaptive Recommendation System for Tourism by Personality Type Using Deep Learning

  • Jeong, Chi-Seo;Lee, Jong-Yong;Jung, Kye-Dong
    • International Journal of Internet, Broadcasting and Communication
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    • v.12 no.1
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    • pp.55-60
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    • 2020
  • Adaptive recommendation systems have been developed with big data processing as a system that provides services tailored to users based on user information and usage patterns. Deep learning can be used in these adaptive recommendation systems to handle big data, providing more efficient user-friendly recommendation services. In this paper, we propose a system that uses deep learning to categorize and recommend tourism types to suit the user's personality. The system was divided into three layers according to its core role to increase efficiency and facilitate maintenance. Each layer consists of the Service Provisioning Layer that real users encounter, the Recommendation Service Layer, which provides recommended services based on user information entered, and the Adaptive Definition Layer, which learns the types of tourism suitable for personality types. The proposed system is highly scalable because it provides services using deep learning, and the adaptive recommendation system connects the user's personality type and tourism type to deliver the data to the user in a flexible manner.

AR Tourism Recommendation System Based on Character-Based Tourism Preference Using Big Data

  • Kim, In-Seon;Jeong, Chi-Seo;Jung, Tae-Won;Kang, Jin-Kyu;Jung, Kye-Dong
    • International Journal of Internet, Broadcasting and Communication
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    • v.13 no.1
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    • pp.61-68
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    • 2021
  • The development of the fourth industry has enabled users to quickly share a lot of data online. We can analyze big data on information about tourist attractions and users' experiences and opinions using artificial intelligence. It can also analyze the association between characteristics of users and types of tourism. This paper analyzes individual characteristics, recommends customized tourist sites and proposes a system to provide the sacred texts of recommended tourist sites as AR services. The system uses machine learning to analyze the relationship between personality type and tourism type preference. Based on this, it recommends tourist attractions according to the gender and personality types of users. When the user finishes selecting a tourist destination from the recommendation list, it visualizes the information of the selected tourist destination with AR.

Deep Learning-based Tourism Recommendation System using Social Network Analysis

  • Jeong, Chi-Seo;Ryu, Ki-Hwan;Lee, Jong-Yong;Jung, Kye-Dong
    • International Journal of Internet, Broadcasting and Communication
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    • v.12 no.2
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    • pp.113-119
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    • 2020
  • Numerous tourist-related data produced on the Internet contain not only simple tourist information but also diverse ideas and opinions from users. In order to derive meaningful information about tourist sites from such big data, the social network analysis of tourist keywords can identify the frequency of keywords and the relationship between keywords. Thus, it is possible to make recommendations more suitable for users by utilizing the clear recommendation criteria of tourist attractions and the relationship between tourist attractions. In this paper, a recommendation system was designed based on tourist site information through big data social network analysis. Based on user personality information, the types of tourism suitable for users are classified through deep learning and the network analysis among tourist keywords is conducted to identify the relationship between tourist attractions belonging to the type of tourism. Tour information for related tourist attractions shown on SNS and blogs will be recommended through tagging.

Tourism Destination Recommender System for the Cold Start Problem

  • Zheng, Xiaoyao;Luo, Yonglong;Xu, Zhiyun;Yu, Qingying;Lu, Lin
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.10 no.7
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    • pp.3192-3212
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    • 2016
  • With the advent and popularity of e-commerce, an increasing number of consumers prefer to order tourism products online. A recommender system can help these users contend with information overload; however, such a system is affected by the cold start problem. Online tourism destination searching is a more difficult task than others on account of its more restrictive factors. In this paper, we therefore propose a tourism destination recommender system that employs opinion-mining technology to refine user preferences and item opinion reputations. These elements are then fused into a hybrid collaborative filtering method by combining user- and item-based collaborative filtering approaches. Meanwhile, we embed an artificial interactive module in our recommender system to alleviate the cold start problem. Compared with several well-known cold start recommendation approaches, our method provides improved recommendation accuracy and quality. A series of experimental evaluations using a publicly available dataset demonstrate that the proposed recommender system outperforms existing recommender systems in addressing the cold start problem.

Card Transaction Data-based Deep Tourism Recommendation Study (카드 데이터 기반 심층 관광 추천 연구)

  • Hong, Minsung;Kim, Taekyung;Chung, Namho
    • Knowledge Management Research
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    • v.23 no.2
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    • pp.277-299
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    • 2022
  • The massive card transaction data generated in the tourism industry has become an important resource that implies tourist consumption behaviors and patterns. Based on the transaction data, developing a smart service system becomes one of major goals in both tourism businesses and knowledge management system developer communities. However, the lack of rating scores, which is the basis of traditional recommendation techniques, makes it hard for system designers to evaluate a learning process. In addition, other auxiliary factors such as temporal, spatial, and demographic information are needed to increase the performance of a recommendation system; but, gathering those are not easy in the card transaction context. In this paper, we introduce CTDDTR, a novel approach using card transaction data to recommend tourism services. It consists of two main components: i) Temporal preference Embedding (TE) represents tourist groups and services into vectors through Doc2Vec. And ii) Deep tourism Recommendation (DR) integrates the vectors and the auxiliary factors from a tourism RDF (resource description framework) through MLP (multi-layer perceptron) to provide services to tourist groups. In addition, we adopt RFM analysis from the field of knowledge management to generate explicit feedback (i.e., rating scores) used in the DR part. To evaluate CTDDTR, the card transactions data that happened over eight years on Jeju island is used. Experimental results demonstrate that the proposed method is more positive in effectiveness and efficacies.

Design of Deep Learning-based Tourism Recommendation System Based on Perceived Value and Behavior in Intelligent Cloud Environment (지능형 클라우드 환경에서 지각된 가치 및 행동의도를 적용한 딥러닝 기반의 관광추천시스템 설계)

  • Moon, Seok-Jae;Yoo, Kyoung-Mi
    • Journal of the Korean Applied Science and Technology
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    • v.37 no.3
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    • pp.473-483
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    • 2020
  • This paper proposes a tourism recommendation system in intelligent cloud environment using information of tourist behavior applied with perceived value. This proposed system applied tourist information and empirical analysis information that reflected the perceptual value of tourists in their behavior to the tourism recommendation system using wide and deep learning technology. This proposal system was applied to the tourism recommendation system by collecting and analyzing various tourist information that can be collected and analyzing the values that tourists were usually aware of and the intentions of people's behavior. It provides empirical information by analyzing and mapping the association of tourism information, perceived value and behavior to tourism platforms in various fields that have been used. In addition, the tourism recommendation system using wide and deep learning technology, which can achieve both memorization and generalization in one model by learning linear model components and neural only components together, and the method of pipeline operation was presented. As a result of applying wide and deep learning model, the recommendation system presented in this paper showed that the app subscription rate on the visiting page of the tourism-related app store increased by 3.9% compared to the control group, and the other 1% group applied a model using only the same variables and only the deep side of the neural network structure, resulting in a 1% increase in subscription rate compared to the model using only the deep side. In addition, by measuring the area (AUC) below the receiver operating characteristic curve for the dataset, offline AUC was also derived that the wide-and-deep learning model was somewhat higher, but more influential in online traffic.

A study on Recommendation Service System for the Customized Convergence Wellness Contents (맞춤형 융복합 웰니스 콘텐츠를 위한 추천 서비스 시스템에 대한 연구)

  • Lee, Wonjin
    • Journal of Korea Multimedia Society
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    • v.20 no.2
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    • pp.322-329
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    • 2017
  • Recently, the importance of personalized healthcare(wellness) services is increasing in the era of the 4th Industrial Revolution. However, the authoring of wellness contents fused with variety of contents and the study of the system which provides the customized recommendation are insufficient. In this paper, we proposes the recommendation service system for the customized convergence wellness contents. The proposed system makes to the wellness contents by the existing cultural/tourism/leisure contents and recommends the customized wellness contents based on a user's profile and the situation information such as location and weather. The proposed systems is expected to contribute to designing the innovative and new service models for the tailored wellness content.

Proposal of Personalized Recommendation for Korean Food and Tour Using Beacon System (비콘을 활용한 개인 맞춤형 한식과 관광지 추천 관리 시스템 제안)

  • Sung, Kihyuk;Ryu, Gihwan;Yun, Daiyeol
    • The Journal of the Convergence on Culture Technology
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    • v.6 no.3
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    • pp.267-273
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    • 2020
  • Beacon is a wireless communication device that can automatically recognize the smart device in the short distance and transmit the necessary data, Beacon is a representative Internet of Things (IoT) facility in the era of the 4th Industrial Revolution, which is utilized in various fields such as short-distance information delivery, mobile location service, shopping, and marketing, and is constantly evolving. In this paper, it is based on tourist site-based recommendation information service. A system is proposed that recommends customized information according to the user's interest, preference, etc. by incorporating beacon technology. In other words, it acts as an information agent that informs tourists of desired information. In order to meet the needs of tourists, it is necessary to build an intelligent tourism recommendation system. The personalized Korean food and tourism recommendation management system using the beacon technology proposed in this paper is expected to provide high-quality services not only to foreigners visiting Korea but also to Korean tourists.

Multiple Fusion-based Deep Cross-domain Recommendation (다중 융합 기반 심층 교차 도메인 추천)

  • Hong, Minsung;Lee, WonJin
    • Journal of Korea Multimedia Society
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    • v.25 no.6
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    • pp.819-832
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    • 2022
  • Cross-domain recommender system transfers knowledge across different domains to improve the recommendation performance in a target domain that has a relatively sparse model. However, they suffer from the "negative transfer" in which transferred knowledge operates as noise. This paper proposes a novel Multiple Fusion-based Deep Cross-Domain Recommendation named MFDCR. We exploit Doc2Vec, one of the famous word embedding techniques, to fuse data user-wise and transfer knowledge across multi-domains. It alleviates the "negative transfer" problem. Additionally, we introduce a simple multi-layer perception to learn the user-item interactions and predict the possibility of preferring items by users. Extensive experiments with three domain datasets from one of the most famous services Amazon demonstrate that MFDCR outperforms recent single and cross-domain recommendation algorithms. Furthermore, experimental results show that MFDCR can address the problem of "negative transfer" and improve recommendation performance for multiple domains simultaneously. In addition, we show that our approach is efficient in extending toward more domains.