Personalized Tourism in Surabaya: A Bayesian Network Approach

This study investigates the application of Bayesian Networks in developing a personalized tourist destination recommendation system focused on Surabaya, Indonesia. The research incorporates push and pulls factors alongside tourist activities as key input variables to model decision-making processes. Two distinct Directed Acyclic Graph (DAG) structures are evaluated: one proposed based on existing theoretical frameworks and another generated from empirical respondent data. The dataset comprises responses from 1,350 tourists visiting twenty-five popular attractions in Surabaya. The analysis reveals that Bayesian Networks effectively identify correlations between various influencing factors. From the tests carried out, the accuracy obtained from the two DAG structures did not significantly differ. The proposed DAG achieved 35% accuracy for the top-ranked destination recommendations, while the data-driven DAG was 25%. Both achieved 75% accuracy in the top five recommendations. The accuracy increased as the number of output states was reduced. Meanwhile, in the test with binary output, BN was able to accurately classify tourist destinations with an average accuracy of 95% for both DAGs. These findings highlight the potential of Bayesian Networks to enhance tourism decision support systems by providing nuanced insights into tourists' preferences and motivations. For further research, hybridization or feature engineering can be employed to improve model accuracy. In addition, determining more appropriate push factors and tourist activities based on the tourism case studies also needs to be done to obtain better tourist preferences. This research highlights the promising role of Bayesian Networks in improving the personalization and effectiveness of tourist recommendations.