KSTR: Keyword-aware skyline travel route recommendation

Yu Ting Wen, Kae Jer Cho, Wen Chih Peng, Jinyoung Yeo, Seung Won Hwang

研究成果: Conference contribution同行評審

20 引文 斯高帕斯(Scopus)

摘要

With 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.

原文English
主出版物標題Proceedings - 15th IEEE International Conference on Data Mining, ICDM 2015
編輯Charu Aggarwal, Zhi-Hua Zhou, Alexander Tuzhilin, Hui Xiong, Xindong Wu
發行者Institute of Electrical and Electronics Engineers Inc.
頁面449-458
頁數10
ISBN(電子)9781467395038
DOIs
出版狀態Published - 5 1月 2016
事件15th IEEE International Conference on Data Mining, ICDM 2015 - Atlantic City, United States
持續時間: 14 11月 201517 11月 2015

出版系列

名字Proceedings - IEEE International Conference on Data Mining, ICDM
2016-January
ISSN(列印)1550-4786

Conference

Conference15th IEEE International Conference on Data Mining, ICDM 2015
國家/地區United States
城市Atlantic City
期間14/11/1517/11/15

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