Geodesic
Tourism Recommendation based on Word Embedding from Card Transaction Data
Computer Science and Information Systems, Tome 20 (2023) no. 3.

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In the tourism industry, millions of card transactions generate a massive volume of big data. The card transactions eventually reflect customers’ consumption behaviors and patterns. Additionally, recommender systems that incorporate users’ personal preferences and consumption is an important subject of smart tourism. However, challenges exist such as handling the absence of rating data and considering spatial factor that significantly affects recommendation performance. This paper applies well-known Doc2Vec techniques to the tourism recommendation. We use them on non-textual features, card transaction dataset, to recommend tourism business services to target user groups who visit a specific location while addressing the challenges above. For the experiments, a card transaction dataset among eight years from Shinhan, which is one of the major card companies in the Republic of Korea, is used. The results demonstrate that the use of vector space representations trained by the Doc2Vec techniques considering spatial information is promising for tourism recommendations.
Keywords: recommender system, word embedding, neural networks, smart tourism 
@article{CSIS_2023_20_3_a3,
     author = {Minsung Hong and Namho Chung and Chulmo Koo},
     title = {Tourism {Recommendation} based on {Word} {Embedding} from {Card} {Transaction} {Data}},
     journal = {Computer Science and Information Systems},
     publisher = {mathdoc},
     volume = {20},
     number = {3},
     year = {2023},
     url = {http://geodesic.mathdoc.fr/item/CSIS_2023_20_3_a3/}
}
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Minsung Hong; Namho Chung; Chulmo Koo. Tourism Recommendation based on Word Embedding from Card Transaction Data. Computer Science and Information Systems, Tome 20 (2023) no. 3. http://geodesic.mathdoc.fr/item/CSIS_2023_20_3_a3/