document: Equip tourists with knowledge mined from travelogues With the prosperity of tourism and Web 2.0 technologies , more and more people have willingness to share their travel experiences on the Web ( e.g. , weblogs , forums , or Web 2.0 communities ) . These so-called travelogues contain rich information , particularly including location-representative knowledge such as attractions ( e.g. , Golden Gate Bridge ) , styles ( e.g. , beach , history ) , and activities ( e.g. , diving , surfing ) . The location-representative information in travelogues can greatly facilitate other tourists ' trip planning , if it can be correctly extracted and summarized . However , since most travelogues are unstructured and contain much noise , it is difficult for common users to utilize such knowledge effectively . In this paper , to mine location-representative knowledge from a large collection of travelogues , we propose a probabilistic topic model , named as Location-Topic model . This model has the advantages of ( 1 ) differentiability between two kinds of topics , i.e. , local topics which characterize locations and global topics which represent other common themes shared by various locations , and ( 2 ) representation of locations in the local topic space to encode both location-representative knowledge and similarities between locations . Some novel applications are developed based on the proposed model , including ( 1 ) destination recommendation for on flexible queries , ( 2 ) characteristic summarization for a given destination with representative tags and snippets , and ( 3 ) identification of informative parts of a travelogue and enriching such highlights with related images . Based on a large collection of travelogues , the proposed framework is evaluated using both objective and subjective evaluation methods and shows promising results .
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