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dc.contributor.authorWen, Yu-Tingen_US
dc.contributor.authorCho, Kae-Jeren_US
dc.contributor.authorPeng, Wen-Chihen_US
dc.contributor.authorYeo, Jinyoungen_US
dc.contributor.authorHwang, Seung-wonen_US
dc.date.accessioned2017-04-21T06:49:17Z-
dc.date.available2017-04-21T06:49:17Z-
dc.date.issued2015en_US
dc.identifier.isbn978-1-4673-9503-8en_US
dc.identifier.issn1550-4786en_US
dc.identifier.urihttp://dx.doi.org/10.1109/ICDM.2015.37en_US
dc.identifier.urihttp://hdl.handle.net/11536/135960-
dc.description.abstractWith the popularity of social media (e.g., Facebook and Flicker), users could easily share their check-in records and photos during their trips. In view of the huge amount of check-in data and photos in social media, we intend to discover travel experiences to facilitate trip planning. Prior works have been elaborated on mining and ranking existing travel routes from check-in data. We observe that when planning a trip, users may have some keywords about preference on his/her trips. Moreover, a diverse set of travel routes is needed. To provide a diverse set of travel routes, we claim that more features of Places of Interests (POIs) should be extracted. Therefore, in this paper, we propose a Keyword-aware Skyline Travel Route (KSTR) framework that use knowledge extraction from historical mobility records and the user\'s social interactions. Explicitly, we model the "Where, When, Who" issues by featurizing the geographical mobility pattern, temporal influence and social influence. Then we propose a keyword extraction module to classify the POI-related tags automatically into different types, for effective matching with query keywords. We further design a route reconstruction algorithm to construct route candidates that fulfill the query inputs. To provide diverse query results, we explore Skyline concepts to rank routes. To evaluate the effectiveness and efficiency of the proposed algorithms, we have conducted extensive experiments on real location-based social network datasets, and the experimental results show that KSTR does indeed demonstrate good performance compared to state-of-the-art works.en_US
dc.language.isoen_USen_US
dc.titleKSTR: Keyword-aware Skyline Travel Route Recommendationen_US
dc.typeProceedings Paperen_US
dc.identifier.doi10.1109/ICDM.2015.37en_US
dc.identifier.journal2015 IEEE INTERNATIONAL CONFERENCE ON DATA MINING (ICDM)en_US
dc.citation.spage449en_US
dc.citation.epage458en_US
dc.contributor.department交大名義發表zh_TW
dc.contributor.departmentNational Chiao Tung Universityen_US
dc.identifier.wosnumberWOS:000380541000046en_US
dc.citation.woscount0en_US
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