Matches in ScholarlyData for { <https://w3id.org/scholarlydata/inproceedings/www2010/paper/main/504> ?p ?o. }
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- 504 creator changhu-wang.
- 504 creator lei-zhang-2.
- 504 creator qiang-hao.
- 504 creator rui-cai.
- 504 type InProceedings.
- 504 label "Equip Tourists with Knowledge Mined from Travelogues".
- 504 sameAs 504.
- 504 abstract "With the prosperity of tourism and the 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 digest utilize such knowledge effectively. In this paper, to mine location-representative knowledge from a large collection of travelogues, we propose a probabilistic generative 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 different locations, and (2) representation of locations in the local topic space to encode both location-representative knowledge and similarities between various locations. Some novel applications are developed based on the proposed model, including (1) destination recommendation based 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.".
- 504 hasAuthorList authorList.
- 504 isPartOf proceedings.
- 504 keyword "Other novel Web data mining algorithms".
- 504 title "Equip Tourists with Knowledge Mined from Travelogues".