Affiliations: [a] Department of Management Science and Engineering, School of Economics and Management, Southeast University, Jiangsu, Nanjing 210096, Jiangsu, China | [b] Tourism Department, School of Humanities, Southeast University, Nanjing 210096, Jiangsu, China
Correspondence:
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Corresponding author: Chao Huang, Department of Management Science and Engineering, Southeast University, Nanjing, Jiangsu, China. Tel.: +86 138 1406 9012; E-mail: [email protected].
Abstract: With the rise of personalized travel recommendation in recent years, automatic analysis and summary of the tourist attraction is of great importance in decision making for both tourists and tour operators. To this end, many probabilistic topic models have been proposed for feature extraction of tourist attraction. However, existing state-of-the-art probabilistic topic models overlook the fact that tourist attractions tend to have distinct characteristics with respect to specific seasonal context. In this article, we contribute the innovative idea of using seasonal contextual information to refine the characteristics of tourist attractions. Along this line, we first propose STLDA, a season topic model based on latent Dirichlet allocation which can capture meaningful topics corresponding to various seasonal contexts for each attraction. Then, an inference algorithm using Gibbs sampling is put forward to learn the model parameters of our proposed model. In order to verify the effectiveness of STLDA model, we present a detailed experimental study using collected real-world textual data of tourist attractions. The experimental analysis results show that the superiority of STLDA over the basic LDA model in providing a representative and comprehensive summarization related to each tourist attraction. More importantly, it has great significance for improving the level of personalized attraction recommendation.
Keywords: Topic mining, contextual information, personalized attraction recommendation, Bayesian model
