Personalized landmark recommendation algorithm based on language-specific satisfaction prediction using heterogeneous open data sources

This paper proposes a personalized landmark recommendation algorithm based on the prediction of users' satisfaction on landmarks. We have accumulated 270,239 user-generated comments from travel websites of Ctrip, Jaran and TripAdvisor for 196 landmarks in Tokyo, Japan. We find that users do have different satisfaction on landmarks depending on their commonly used languages and travel websites. Then we establish a database for landmarks with abundant and accurate landmark type and landmark satisfaction information. Finally, we propose an effective personalized landmark satisfaction prediction algorithm based on users' landmark type, language and travel website preferences. After that, landmarks with the top-6 highest satisfaction are provided to the user for a one-day visit plan in Tokyo. Experimental results demonstrate that the proposed algorithm can recommend landmarks that fit the user's preferences and our algorithm also successfully predicts the user's landmark satisfaction with a low error rate less than 7%, which is superior to other previous studies.