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Mobile crowdsoured data from location based social network services (LBSNs) provide information on individual's preferences for locations. In this article, we propose a travel package recommendation system to help users make travel plans by leveraging mobile crowdsourced data. We extract user preferences, discover points of interest (POIs), and determine location correlations from check-in records. We then generate personalized travel packages by considering user preferences, POI characteristics, and temporalspatial constraints such as travel time and starting location. A prototype system is built and evaluated on real-world crowdsourced data from Jie Pang, one of the most popular LBSNs in China.
Yu et al. (Fri,) studied this question.
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