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http://dx.doi.org/10.15813/kmr.2022.23.2.014

Card Transaction Data-based Deep Tourism Recommendation Study  

Hong, Minsung (Smart Tourism Research Center, Kyung-Hee University)
Kim, Taekyung (Division of Business Administration, Kwangwoon University)
Chung, Namho (Smart Tourism Education Platform, Kyung-Hee University)
Publication Information
Knowledge Management Research / v.23, no.2, 2022 , pp. 277-299 More about this Journal
Abstract
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.
Keywords
Smart tourism recommendation; Deep learning; Resource description framework; RFM analysis;
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