標題: KSTR: Keyword-aware Skyline Travel Route Recommendation
作者: Wen, Yu-Ting
Cho, Kae-Jer
Peng, Wen-Chih
Yeo, Jinyoung
Hwang, Seung-won
交大名義發表
National Chiao Tung University
公開日期: 2015
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.
URI: http://dx.doi.org/10.1109/ICDM.2015.37
http://hdl.handle.net/11536/135960
ISBN: 978-1-4673-9503-8
ISSN: 1550-4786
DOI: 10.1109/ICDM.2015.37
期刊: 2015 IEEE INTERNATIONAL CONFERENCE ON DATA MINING (ICDM)
起始頁: 449
結束頁: 458
顯示於類別:會議論文