Data Gathering System for Recommender System in Tourism

Hirakawa, Go; Satoh, Goshi; Hisazumi, Kenji; Shibata, Yoshitaka

doi:10.1109/nbis.2015.78

Cited by 15 publications

(4 citation statements)

References 4 publications

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“…The basic architecture of a typical recommendation system is based upon three modules that serve for its knowledge basis named Context, User Profile and Content [5]. Fig.…”

Section: A Recommendation System (Rs) and Its Componentsmentioning

confidence: 99%

Influential Factors for Tourist Profiling for Personalized Tourism Recommendation Systems– A Compact Survey

Kamal

Chatzigiannakis

2021

2021 International Conference on Innovative Computing (ICIC)

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Over the past few years, recommendation systems have gained immense popularity and are being applied to many domains. This paper discusses the application of recommendation systems for the domain of tourism. In this paper, we aim to present an up-to-date, thorough and compact survey on Tourism Recommendation Systems and the most influential factors which significantly impacts the decisionmaking of tourists while planning a tour. We have discussed complete architecture and modules of recommendation systems, various types of recommendation approaches and algorithms, the functionalities offered by these systems, the factors used for recommendation and knowledge base, tourist profiling and type of data used in tourism recommendation systems. The paper also mentions the scope of improvement and promising areas of research in this field.

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“…The basic architecture of a typical recommendation system is based upon three modules that serve for its knowledge basis named Context, User Profile and Content [5]. Fig.…”

Section: A Recommendation System (Rs) and Its Componentsmentioning

confidence: 99%

Influential Factors for Tourist Profiling for Personalized Tourism Recommendation Systems– A Compact Survey

Kamal

Chatzigiannakis

2021

2021 International Conference on Innovative Computing (ICIC)

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“…5, October 2019 : 4460 -4465 4462 crucial task to collect appropriate data from a vast amount of information for a recommender system. The paper of [18] proposed the architecture of tourist support information system which includes VR contents to promote Iwate area in Japan and gathers content repository and training data to build regional specific recommender engine on the tourist support system. The authors in [19] utilise textual information from users reviews and use ad-hoc and regression-based recommendation measures to give a personalised rating value.…”

Section: Literature Reviewmentioning

confidence: 99%

Recommender system for personalised travel itinerary

Tulasi

2019

IJECE

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<p class="Body">A recommender system is an approach to give an appropriate solu-tion to a particular problem. This helps in recognising the pattern or behaviour of a user to suggest future possible likes of the user. Nowa-days people like to travel during their spare time, it has become a rigid task to decide where to go. This paper represents a customised recommender system to help users in destining their itinerary. A model is designed to suggest the best places to visit in Rome. A questionnaire was prepared to get information about users interest during their travel. The model generates the best five places to visit with respect to the choice picked by the user. The top five places for each category will be displayed to the user and the user was asked to pick a starting point for the itinerary. Then the model generates another set off a filtered list of places to enhance their travel experi-ence. It includes displaying the top 5 restaurants to visit during their travel.</p>

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“…In this paper they have introduced an architecture of tourist support information system including VR contents that are aimed to promote Iwate area in Japan [3]. They proposed a system for gathering contents repository and training data to construct regional specific recommender engine on tourist support system.…”

Section: Go Hirakawa Goshi Sato Kenji Hisazumi Yoshitaka Shibata [3]mentioning

confidence: 99%

Survey Paper on Wayfarer: A Travel Recommendation System

Gatade¹,

Jain²,

Chandarana³

et al. 2017

International Journal of Advanced Research in Computer and Comm

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In tourism recommendation system, a number of users and items are very large. But traditional recommendation system uses partial information for identifying similar characteristics of users. Collaborative filtering is a primary approach of any recommendation system. We are using the SentiWordNet algorithm for giving ratings to search destinations. Social networking sites act as an interactive platform for today's generation. So a lot can be extracted from social networking sites for recommendations. In the considered system, semantic positive and negative values are being calculated from SentiWordNet. Logged user can apply different filters to search destination. Captions will be extracted through social networking site and upon that text mining will be performed and trending spots will be provided to the customer through a web portal. Real-time data from Instagram is being accessed and process through spring boot. We have created APIs using RESTful web services for a web-based application.

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Data Gathering System for Recommender System in Tourism

Cited by 15 publications

References 4 publications

Influential Factors for Tourist Profiling for Personalized Tourism Recommendation Systems– A Compact Survey

Influential Factors for Tourist Profiling for Personalized Tourism Recommendation Systems– A Compact Survey

Recommender system for personalised travel itinerary

Survey Paper on Wayfarer: A Travel Recommendation System

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