Skip to main content
SpringerLink
Log in

Diversifying Top-k Routes with Spatial Constraints

  • Regular Paper
  • Published:
Journal of Computer Science and Technology Aims and scope Submit manuscript

Abstract

Trip recommendation has become increasingly popular with the rapid growth of check-in data in location-based social networks. Most existing studies focused only on the popularity of trips. In this paper, we consider further the usability of trip recommendation results through spatial diversification. We thereby formulate a new type of queries named spatial diversified top-k routes (SDkR) query. This type of queries finds k trip routes with the highest popularity, each of which starts at a given starting point, consumes travel time within a given time budget, and passes through points of interest (POIs) of given categories. Any two trip routes returned are diversified to a certain degree defined by the spatial distance between the two routes. We show that the SDkR problem is NP-hard. We propose two precise algorithms to solve the problem. The first algorithm starts with identifying all candidate routes that satisfy the query constraints, and then searches for the k-route combination with the highest popularity. The second algorithm identifies the candidate routes and builds up the optimal k-route combination progressively at the same time. Further, we propose an approximate algorithm to obtain even higher query efficiency with precision bounds. We demonstrate the effectiveness and efficiency of the proposed algorithms on real datasets. Our experimental results show that our algorithms find popular routes with diversified POI locations. Our approximate algorithm saves up to 90% of query time compared with the baseline algorithms.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Su H, Zheng K, Huang J M, Liu T Y, Wang H Z, Zhou X F. A crowd-based route recommendation system — Crowd-Planner. In Proc. the 30th International Conference on Data Engineering, March 2014, pp.1178-1181.

  2. Lu H C, Chen C Y, Tseng V S. Personalized trip recommendation with multiple constraints by mining user check-in behaviors. In Proc. the 20th International Conference on Advances in Geographic Information Systems, November 2012, pp.209-218.

  3. Zhang C Y, Liang H W, Wang K, Sun K L. Personalized trip recommendation with PoI availability and uncertain traveling time. In Proc. the 24th ACM International Conference on Information and Knowledge Management, October 2015, pp.911-920.

  4. Hsieh H P, Li C T. Mining and planning time-aware routes from check-in data. In Proc. the 23rd International Conference on Information and Knowledge Management, November 2014, pp.481-490.

  5. Shang S, Ding R G, Yuan B, Xie K X, Zheng K, Kalnis P. User oriented trajectory search for trip recommendation. In Proc. the 15th International Conference on Extending Database Technology, March 2012, pp.156-167.

  6. Dai J, Liu C F, Xu J J, Ding Z M. On personalized and sequenced route planning. World Wide Web: Internet and Web Information Systems, 2016, 19(4): 679-705.

    Article  Google Scholar 

  7. Tang J Y, Sanderson M. Spatial diversity, do users appreciate it? In Proc. the 6th Workshop on Geographic Information Retrieval, February 2010, Article No. 22.

  8. Chen Z B, Shen H T, Zhou X F, Zheng Y, Xie X. Searching trajectories by locations: An efficiency study. In Proc. the 29th ACM SIGMOD International Conference on Management of Data, June 2010, pp.255-266.

  9. Shang S, Chen L S, Jensen C S, Wen J R, Kalnis P. Searching trajectories by regions of interest. IEEE Transactions on Knowledge and Data Engineering, 2017, 29(7): 1549-1562.

    Article  Google Scholar 

  10. Shang S, Ding R, Zheng K, Jensen C S, Kalnis P, Zhou X F. Personalized trajectory matching in spatial networks. The International Journal on Very Large Data Bases, 2014, 23(3): 449-468.

    Article  Google Scholar 

  11. Zheng K, Zheng B L, Xu J J, Liu G F, An L, Li Z X. Popularity-aware spatial keyword search on activity trajectories. World Wide Web: Internet and Web Information Systems, 2017, 20(4): 749-773.

    Article  Google Scholar 

  12. Zheng K, Yang Y, Shang S, Yuan N J. Towards efficient search for activity trajectories. In Proc. the 29th International Conference on Data Engineering, April 2013, pp.230-241.

  13. Shang S, Chen L S, Zheng K, Jensen C S, Wei Z, Kalnis P. Parallel trajectory-to-location join. IEEE Transactions on Knowledge and Data Engineering. doi: https://doi.org/10.1109/TKDE.2018.2854705.

  14. Wei L Y, Zheng Y, Peng W C. Constructing popular routes from uncertain trajectories. In Proc. the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2012, pp.195-203.

  15. Cao X, Chen L S, Cong G, Xiao X K. Keyword-aware optimal route search. Proceedings of the VLDB Endowment, 2012, 5(11): 1136-1147.

    Article  Google Scholar 

  16. Li Y J, Yang W D, Dan W, Xie Z P. Keyword-aware dominant route search for various user preferences. In Proc. the 20th International Conference on Database Systems for Advanced Applications, April 2015, pp.207-222.

  17. Shang S, Chen L S, Wei Z W, Jensen C S, Wen J R, Kalnis P. Collective travel planning in spatial networks. IEEE Transactions on Knowledge and Data Engineering, 2016, 28(5): 1132-1146.

    Article  Google Scholar 

  18. Wen Y T, Yeo J, Peng WC, Hwang SW. Efficient keyword-aware representative travel route recommendation. IEEE Transactions on Knowledge and Data Engineering, 2018, 29(8): 1639-1652.

    Article  Google Scholar 

  19. Liu H, Jin C Q, Yang B, Zhou A Y. Finding top-k optimal sequenced routes. In Proc. the 34th International Conference on Data Engineering, April 2018, pp.569-580.

  20. Shang S, Liu J, Zheng K, Lu H, Pedersen T B, Wen J R. Planning unobstructed paths in traffic-aware spatial networks. Geo Informatica, 2015, 19(4): 723-746.

    Google Scholar 

  21. Soma S C, Hashem T, Cheema M A, Samrose S. Trip planning queries with location privacy in spatial databases. World Wide Web: Internet and Web Information Systems, 2017, 20(2): 205-236.

    Article  Google Scholar 

  22. Zheng B L, Su H, Hua W, Zheng K, Zhou X F, Li G H. Efficient clue-based route search on road networks. IEEE Transactions on Knowledge and Data Engineering, 2017, 29(9): 1846-1859.

    Article  Google Scholar 

  23. Xu J J, Gao Y J, Liu C F, Zhao L, Ding Z M. Efficient route search on hierarchical dynamic road networks. Distributed and Parallel Databases, 2015, 33(2): 227-252.

    Article  Google Scholar 

  24. Liang S S, Yilmaz E, Shen H, Rijke M D, Croft W B. Search result diversification in short text streams. ACM Transactions on Information Systems, 2017, 36(1): Article No. 8.

  25. Angel A, Koudas N. Efficient diversity-aware search. In Proc. the 30th ACM SIGMOD International Conference on Management of Data, June 2011, pp.781-792.

  26. Khan H A, Sharaf M A. Model-based diversification for sequential exploratory queries. Data Science and Engineering, 2017, 2(2): 151-168.

    Article  Google Scholar 

  27. Chen L S, Cong G. Diversity-aware top-k publish/subscribe for text stream. In Proc. the 34th ACM SIGMOD International Conference on Management of Data, May 2015, pp.347-362.

  28. Fan W F, Wang X, Wu Y H. Diversified top-k graph pattern matching. Proceedings of the VLDB Endowment, 2013, 6(13): 1510-1521.

    Article  Google Scholar 

  29. Yuan L, Qin l, Lin X M, Chang L J, Zhang W J. Diversified top-k clique search. The International Journal on Very Large Data Bases, 2016, 25(2): 171-196.

  30. Carbonell J G, Goldstein-Stewart J. The use of MMR, diversity-based reranking for reordering documents and producing summaries. In Proc. the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, August 1998, pp.335-336.

  31. Vieira M R, Razente H L, Barioni M C, Hadjieleftheriou M, Srivastava D, Traina C, Tsotras V J. On query result diversification. In Proc. the 27th International Conference on Data Engineering, April 2011, pp.1163-1174.

  32. Qin L, Yu Y J, Chang L J. Diversifying top-k results. Proceedings of the VLDB Endowment, 2012, 5(11): 1124-1135.

    Article  Google Scholar 

  33. Garey M R, Johnson D S. Computers and intractability: A guide to the theory of NP-completeness. Society for Industrial and Applied Mathematics, 1982, 24(1): 90-91.

    Google Scholar 

  34. Jain A, Sarda P, Haritsa J R. Providing diversity in k-nearest neighbor query results. In Proc. the 8th Pacific-Asia Conference on Knowledge Discovery and Data Mining, May 2004, pp.404-413.

  35. Lee K C K, Lee W C, Leong H V. Nearest surrounder queries. IEEE Transactions on Knowledge and Data Engineering, 2010, 22(10): 1444-1458.

    Article  Google Scholar 

  36. Kucuktunc O, Ferhatosmanoglu H. λ-diverse nearest neighbors browsing for multidimensional data. IEEE Transactions on Knowledge and Data Engineering, 2013, 25(3): 481-493.

    Article  Google Scholar 

  37. Ference G, Lee WC, Jung H J, Yang D N. Spatial search for K diverse-near neighbors. In Proc. the 22nd ACM International Conference on Information and Knowledge Management, October 2013, pp.19-28.

  38. Zhang C Y, Zhang Y, Zhang W J, Lin X M, Cheema M A, Wang X Y. Diversified spatial keyword search on road networks. In Proc. the 17th International Conference on Extending Database Technology, March 2014, pp.367-378.

  39. Godsil C, Royle G F. Algebraic Graph Theory. Springer, 2001.

  40. Chiba N, Nishizeki T. Arboricity and subgraph listing algorithms. SIAM Journal on Computing, 1985, 14(1): 210-223.

    Article  MathSciNet  MATH  Google Scholar 

  41. Bao J, Zheng Y, Mokbel M F. Location-based and preference-aware recommendation using sparse geo-social networking data. In Proc. the 20th International Conference on Advances in Geographic Information Systems, November 2012, pp.199-208.

Download references

Author information

Authors and Affiliations

  1. College of Computer Science and Engineering, Northeastern University, Shenyang, 110169, China

    Hong-Fei Xu, Yu Gu & Ge Yu

  2. Department of Computing and Information Systems, The University of Melbourne, Melbourne, VIC, 3010, Australia

    Jian-Zhong Qi & Jia-Yuan He

Authors
  1. Hong-Fei Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yu Gu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jian-Zhong Qi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jia-Yuan He
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ge Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ge Yu.

Electronic supplementary material

ESM 1

(PDF 223 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xu, HF., Gu, Y., Qi, JZ. et al. Diversifying Top-k Routes with Spatial Constraints. J. Comput. Sci. Technol. 34, 818–838 (2019). https://doi.org/10.1007/s11390-019-1944-6

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11390-019-1944-6

Keywords

Search

Navigation