Mining coterie patterns from Instagram photo trajectories for recommending popular travel routes

Abstract

Instagram is a popular photo-sharing social application. It is widely used by tourists to record their journey information such as location, time and interest. Consequently, a huge volume of geo-tagged photos with spatio-temporal information are generated along tourist’s travel trajectories. Such Instagram photo trajectories consist of travel paths, travel density distributions, and traveller behaviors, preferences, and mobility patterns. Mining Instagram photo trajectories is thus very useful for many mobile and location-based social applications, including tour guide and recommender systems. However, we have not found any work that extracts interesting group-like travel trajectories from Instagram photos asynchronously taken by different tourists. Motivated by this, we propose a novel concept:coterie, which reveals representative travel trajectory patterns hidden in Instagram photos taken by users at shared locations and paths. Our work includes the discovery of (1)coteries, (2) closed coteries, and (3) the recommendation of popular travel routes based on closed coteries. For this, we first build a statistically reliable trajectory database from Instagram geo-tagged photos. These trajectories are then clustered by the DBSCAN method to find tourist density. Next, we transform each raw spatio-temporal trajectory into a sequence of clusters. All discriminative closedcoteriesare further identified by a Cluster-Growth algorithm. Finally, distance-aware and conformityaware recommendation strategies are applied onclosed coteriesto recommend popular tour routes. Visualized demos and extensive experimental results demonstrate the effectiveness and efficiency of our methods.

Keywords

tourists / coterie / closed coterie / geotagged photos / Instagram trajectories / recommendation / popular travel routes

References

[1] BenkertM, Gudmundsson J, HubnerF , WolleT. Reporting flock patterns. Computational Geometry, 2008, 41(3): 111–125

[2] JeungH, YiuM L, ZhouX F, Jensen C S, ShenH T . Discovery of convoys in trajectory databases. Proceedings of the VLDB Endowment, 2008, 1(1): 1068–1080

[3] LiZ H, DingB, HanJ W, Kays R. Swarm: mining relaxed temporal moving object clusters. Proceedings of the VLDB Endowment, 2010, 3(1): 723–734

[4] ZhengK, ZhengY, YuanN J, Shang S. On discovery of gathering patterns from trjaectories. In: Proceedings of the 29th IEEE International Conference on Data Engineering. 2013, 242–253

[5] AraseY, XieX, HaraT, Nishio S. Mining peoples’s trips from large scale geo-tagged photos. In: Proceedings of the 18th ACM International Conference on Multimedia. 2010, 133–142

[6] ChenY Y, ChengA J, HsuW H. Travel recommendation by mining people attributes and travel group types from commutiny-contributed photos. IEEE Transactions on Multimedia, 2013, 15(6): 1283–1295

[7] ZhengY, ZhangL Z, XieX, Ma W Y. Mining interesting locations and travel sequences from GPS trajectories. In: Proceedings of the 18th International Conference on World Wide Web. 2009, 791–800

[8] MaS, ZhengY, WolfsonO. T-share: a large-scale dynamic taxi ridesharing service. In: Proceedings of the 29th IEEE International Conference on Data Engineering. 2013, 410–421

[9] LiuS Y, WangS H, JayarajahK, Misra A, KrishnanR . TODMIS: mining communities from trajectories. In: Proceedings of the 22nd ACM International Conference on Information and Knowledge Management. 2013, 2109–2118

[10] GengX L, Arimura H, UnoT . Pattern mining from trajectory GPS data. In: Proceedings of IIAI International Conference on e-Services and Knowledge Management. 2012, 60–65

[11] GiannottiF, NanniM, PedreschiD, Pinelli F, RensoC , RinzivilloS, Trasarti R. Unveiling the complexity of human mobility by querying and mining massive trajectory data. The VLDB Journal — The International Journal on Very Large Data Bases, 2011, 20(5): 695–719

[12] ZhengY, ZhangL Z, XieX, Ma W Y. Mining correlation between locations using human location history. In: Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. 2009, 472–475

[13] EsterM, Kriegel H P, SanderJ , XuX. A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining. 1996, 226–231

[14] YZheng. Trajectory data mining: an overview. ACM Transactions on Intelligent Systems and Technology, 2015, 6(3): 29

[15] KnuthD E. The art of computer programming, volume 4, fascicle 1. Bitwise tricks & techniques; Binary decision diagrams. New Jersey: Addison-Wesley Professional, 2009

[16] PrimR C. Shortest connection networks and some generalizations. Bell System Technical Journal, 1957, 36(6): 1389–1401

[17] PageL, BrinS, MotwaniR, Winograd T. The PageRank citation ranking: bringing order to the Web. Stanford InfoLab Technical Report SIDL-WP-1999-0120. 1999

[18] GaffneyS, SmythP. Trajectory clustering with mixtures of regression models. In: Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 1999, 63–72

[19] LeeJ G, HanJ W, WhangK Y. Trajectory clustering: a partition-andgroup framework. In: Proceedings of the ACM SIGMOD International Conference on Management of Data. 2007, 593–604

[20] LiZ H, LeeJ G, LiX L, Han J W. Incremental clustering for trajectories. In: Proceedings of the 15th International Conference of Database System for Advanced Applications. 2010, 32–46

[21] AssentI, Krieger R, MullerE , SeidlT. INSCY: indexing subspace clusters with in-process-removal of redundancy. In: Proceedings of the 8th IEEE International Conference on Data Mining. 2008, 719–724

[22] KrogerP, Kriegel H P, KailingK . Density-connected subspace clustering for high-dimensional data. In: Proceedings of the 4th SIAM International Conference on Data Mining. 2010, 246–256

[23] ZhengV W, ZhengY, XieX, Yang Q. Collaborative location and activity recommendations with GPS history data. In: Proceedings of the 19th International Conference on World Wide Web. 2010. 1029–1038

[24] ZhengY T, ZhaZ J, ChuaT S. Mining travel patterns from geotagged photos. ACM Transactions on Intelligent Systems and Technology, 2012, 3(3): 56

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