KSTR: Keyword-aware skyline travel route recommendation

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

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    20 Citations (Scopus)


    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.

    Original languageEnglish
    Title of host publicationProceedings - 15th IEEE International Conference on Data Mining, ICDM 2015
    EditorsCharu Aggarwal, Zhi-Hua Zhou, Alexander Tuzhilin, Hui Xiong, Xindong Wu
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Number of pages10
    ISBN (Electronic)9781467395038
    Publication statusPublished - 2016 Jan 5
    Event15th IEEE International Conference on Data Mining, ICDM 2015 - Atlantic City, United States
    Duration: 2015 Nov 142015 Nov 17

    Publication series

    NameProceedings - IEEE International Conference on Data Mining, ICDM
    ISSN (Print)1550-4786


    Other15th IEEE International Conference on Data Mining, ICDM 2015
    Country/TerritoryUnited States
    CityAtlantic City

    All Science Journal Classification (ASJC) codes

    • Engineering(all)


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