NDLI: KSTR: Keyword-Aware Skyline Travel Route Recommendation

Content Provider	IEEE Xplore Digital Library
Author	Yu-Ting Wen Kae-Jer Cho Wen-Chih Peng Jinyoung Yeo Seung-won Hwang
Copyright Year	2015
Description	Author affiliation: Yonsei Univ., Yonsei, South Korea (Seung-won Hwang) \|\| Nat. Chiao Tung Univ., Hsinchu, Taiwan (Yu-Ting Wen; Kae-Jer Cho; Wen-Chih Peng) \|\| Pohang Univ. of Sci. & Technol., Pohang, South Korea (Jinyoung Yeo)
Abstract	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.
Starting Page	449
Ending Page	458
File Size	781023
Page Count	10
File Format	PDF
ISSN	15504786
e-ISBN	9781467395045
DOI	10.1109/ICDM.2015.37
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-11-14
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Trajectory Planning Radio frequency Feature extraction Semantics Social network services Media
Content Type	Text

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