With the popularity of social media such as Facebook and Flicker, users can easily share their registration records and photos during their travels. When planning a trip, users always have a particular preference for their trip. The system does not restrict users to limited query options (such as location, activity, or time period), but rather treats any text description as a keyword for personalized needs. Previous work has elaborated on excavating and arranging existing routes from check-in data. In order to meet the needs of the auto travel organization, the system claims that more POIs should be extracted. Therefore, this paper proposes an efficient keyword-based representative travel route framework that uses knowledge from historical flow records and social interactions of users. Explicitly, the system designs a keyword extraction module to classify POI-related tags so as to effectively match the query keywords. The system further designed a route reconstruction algorithm to build route candidates that meet the requirements. In order to provide appropriate search results, explore the representative Skyline concept, the Skyline route, which best describes the tradeoffs between different POI features. In order to evaluate the effectiveness and efficiency of the proposed algorithm, extensive experiments were conducted on location-based real social network datasets. However, it does not consider the cost of travel routes. So use the minimum spanning tree to calculate cost-effective travel routes.
Location-based social network, text mining, travel route recommendation
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