Personalized POIs travel route recommendation system based on tourism big data

Abstract

One of the most important travel preparation activities for tourists is to plan a personalization POIs travel route to a new city, and it is yet a challenging task however. In this paper, we first propose a novel method to integrating multi-source tourism big data on websites to generate POIs knowledgebase and POIs visit sequences. Then a POI-Visit pattern sequence mining algorithm is proposed to generate various candidate POIs travel route. Last, the POIs travel route recommendation method is designed to provide a list of personalization POIs travel routes under tourist personal constraints, including the travel duration, the type of companion in trip, the visit season and the preferring tourism types etc. To validate the proposed system, extensive experiments are conducted on real tourism data set related to the city of Guilin in China, which contains 10,109 real travelogues, 132 POIs profiles and 8,646 POI traffic times.