TPEDTR: temporal preference embedding-based deep tourism recommendation with card transaction data

Recently, the recommender system has been raised as one of the essential research topics in smart tourism. The massive card transaction data generated in the tourism industry has become an important resource that implies tourist consumption behaviors and patterns. However, there are challenges such as the high absence possibility of explicit feedback, which is the basis of traditional collaborative filtering techniques, and the consideration of auxiliary factors (e.g., temporal, spatial, and demographic information) that could improve the recommendation performances. In this paper, we introduce TPEDTR, a novel approach using card transaction data to recommend tourism services. It consists of two main components: (i) temporal preference embedding (TPE) models tourist groups’ interactions with services chronologically to obtain their representation vectors. And (ii) deep neural network-based tourism recommendation (DTR) uses the vectors and auxiliary factors as inputs to provide tourist services. To evaluate the TPEDTR, a dataset of card transactions that happened in Jeju island, one of the most famous attractions in South Korea, over eight years is used. Experimental results demonstrate the efficacy of the proposed method and the positive effectiveness of introducing additional information on recommendation performances.