Abstract
Recommending travel destinations on the basis of users' travel intentions is a research topic being studied recently in the field of intention analysis. This study considers travel intentions from a large number of travel-related reviews containing the reviewers' purpose for visiting the points of interest (POIs). We analyze travel intentions of 83,207 POIs using 6,791,427 reviews in www.TripAdvisor.com with domain-tailored word embedding model. Building an attraction network based on travel intentions helps to recommend travel destinations to travelers and reviewers. We present three prediction methods to recommend travel destinations with an attraction network and description logic. We also present the evaluation results of recommendations from some prediction scenarios. Consequently, the travel intention classification is commensurate with an analysis of intentions from textual data, and the attraction network is useful for recommending travel destinations on the basis of short-and long-term user preferences.
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