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Data Gathering System for Recommender System in Tourism

Authors: Go Hirakawa; Goshi Sato; Kenji Hisazumi; Yoshitaka Shibata;

Data Gathering System for Recommender System in Tourism

Abstract

The advance in the mobile terminal and wireless communication environment enables practical use of recommendation systems in tourism. A recommender system is a kind of information filtering system that helps users to find the optimal information from vast amounts of information using user preferences, environmental contexts, and user contexts. To carries out the optimum recommendation, Recommender system requires well-developed contents repository and a large amount of labeled training data. However, such travel contents and travel context information as training data has not fully digitized even in famous tourist cities let alone in the countryside. In this paper, we introduce an architecture of a tourist support information system including VR contents that are aimed at promoting Iwate area in Japan. Also, we propose a system for gathering contents repository and training data to construct regional specific recommender engine on the tourist support system.

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    popularity
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    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
10
Top 10%
Top 10%
Average
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