Recommendation of Heterogeneous Cultural Heritage Objects for the Promotion of Tourism

被引:4
|
作者
Rajaonarivo, Landy [1 ,3 ]
Fonteles, Andre [2 ]
Sallaberry, Christian [1 ]
Bessagnet, Marie-Noelle [1 ]
Roose, Philippe [1 ]
Etcheverry, Patrick [1 ]
Marquesuzaa, Christophe [1 ]
Lacayrelle, Annig Le Parc [1 ]
Cayere, Cecile [1 ]
Coudert, Quentin [1 ]
机构
[1] Univ Pau & Pays Adour, Lab Informat, F-64000 Pau, France
[2] Indiana Wesleyan Univ, Math & Comp Informat Sci, Marion, IN 46953 USA
[3] Univ Pau & Pays Adour, EA3000, E2S UPPA, Lab Informat, F-64000 Pau, France
关键词
hybrid recommender systems; itinerary recommendation; context awareness; tourism recommendation; PERSONALIZED RECOMMENDATION; BAYESIAN NETWORK; SYSTEM; ATTRACTIONS; CONTEXT; MODEL;
D O I
10.3390/ijgi8050230
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The cultural heritage of a region, be it a highly visited one or not, is a formidable asset for the promotion of its tourism. In many places around the world, an important part of this cultural heritage has been catalogued by initiatives backed by governments and organisations. However, as of today, most of this data has been mostly unknown, or of difficult access, to the general public. In this paper, we present research that aims to leverage this data to promote tourism. Our first field of application focuses on the French Pyrenees. In order to achieve our goal, we worked on two fronts: (i) the ability to export this data from their original databases and data models to well-known open data platforms; and (ii) the proposition of an open-source algorithm and framework capable of recommending a sequence of cultural heritage points of interests (POIs) to be visited by tourists. This itinerary recommendation approach is original in many aspects: it not only considers the user preferences and popularity of POIs, but it also integrates different contextual information about the user as well as the relevance of specific sequences of POIs (strong links between POIs). The ability to export the cultural heritage data as open data and to recommend sequences of POIs are being integrated in a first prototype.
引用
收藏
页数:25
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