Location Based Recommender Systems (LBRS) – A Review | SpringerLink
Skip to main content

Location Based Recommender Systems (LBRS) – A Review

  • Conference paper
  • First Online:
Computational Intelligence in Data Science (ICCIDS 2020)

Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 578))

Included in the following conference series:

  • 480 Accesses

Abstract

Recommender system has a vital role in everyday life with newer advancement. Location based recommender system is the current trend involved in mobile devices by providing the user with their timely needs in an effective and efficient manner. The services provided by the location based recommender system are Geo-tagged data based services containing the Global Positioning System and sensors incorporated to accumulate user information. Bayesian network model is widely used in geo-tagged based services to provide solution to the cold start problem. Point Location based services considers user check-in and auxiliary information to provide recommendation. Regional based recommendation can be considered for improving accuracy in this Point location based service. Trajectory based services uses the travel paths of the user and finds place of interest along with the similar user behaviours. Context based information can be incorporated with these services to provide better recommendation. Thus this article provides an overview of the Geo-tagged media based services and Point Location based services and discusses about the possible research issues and future work that can be implemented.

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

Access this chapter

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
JPY 3498
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
JPY 11439
Price includes VAT (Japan)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
JPY 14299
Price includes VAT (Japan)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
JPY 14299
Price includes VAT (Japan)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Tiwari, S., Kaushik, S., Jagwani, P.: Location based recommender systems: architecture, trends and research areas. In: IET International Conference on Wireless Communications and Applications (ICWCA 2012), pp. 1–8. IET (2012). https://doi.org/10.1049/cp.2012.2096

  2. Bao, J., Zheng, Y., Wilkie, D., Mokbel, M.: Recommendations in location-based social networks: a survey. GeoInformatica 19(3), 525–565 (2015). https://doi.org/10.1007/s10707-014-0220-8

    Article  Google Scholar 

  3. Park, M., Hong, J., Cho, S.: Location-based recommendation system using Bayesian User’ s preference model in mobile devices. Expert Syst. Appl. 39(11), 1130–1139 (2012). https://doi.org/10.1016/j.eswa.2012.02.038

    Article  Google Scholar 

  4. Logesh, R., Subramaniyaswamy, V., Vijayakumar, V., Li, X.: Efficient user profiling based intelligent travel recommender system for individual and group of users. Mob. Netw. Appl. 24(3), 1018–1033 (2018). https://doi.org/10.1007/s11036-018-1059-2

    Article  Google Scholar 

  5. Lu, X., Wang, C., Yang, J., Pang, Y., Zhang, L.: Photo2Trip : generating travel routes from geo-tagged photos for trip planning. In: Proceedings of the 18th ACM International Conference on Multimedia, pp. 143–152. (2010). https://doi.org/10.1145/1873951.1873972

  6. Zheng, Y., Zhang, L., Xie, X., Ma, W.: Mining interesting locations and travel sequences from GPS trajectories. In: Proceedings of the 18th International Conference on World Wide Web, pp. 791–800 (2009). https://doi.org/10.1145/1526709.1526816

  7. Majid, A., Chen, L., Chen, G., Mirza, H.T., Hussain, I., Woodward, J.: A context-aware personalized travel recommendation system based on geotagged social media data mining. Int. J. Geogr. Inf. Sci. 37–41 (2012). https://doi.org/10.1080/13658816.2012.696649

  8. Chon, Y., Cha, H.: LifeMap: a smartphone-based context provider for location-based services. IEEE Perv. Comput. 10(2), 58–67 (2011). https://doi.org/10.1109/mprv.2011.13

    Article  Google Scholar 

  9. Rehman, F., Khalid, O., Madani, S.: A comparative study of location based recommendation systems. Knowl. Eng. Rev. 31, 1–30 (2017). https://doi.org/10.1017/S0269888916000308

    Article  Google Scholar 

  10. Zhang, C., Liu, L., Lei, D., Zhuang, H., Hanra, T., Han, J.: TrioVecEvent: embedding-based online local event detection in geo-tagged tweet streams. Knowl. Data Discov. 595–605 (2017). https://doi.org/10.1145/3097983.3098027

  11. Yang, L., Wu, L., Liu, Y., Kang, C.: Quantifying tourist behavior patterns by travel motifs and geo-tagged photos from Flickr. SPRS Int. J. Geo-Informatica 6(11), 1–18 (2017). Article number 345. https://doi.org/10.3390/ijgi6110345

  12. Sun, X., Huang, Z., Peng, X., Chen, Y., Liu, Y.: Building a model-based personalised recommendation approach for tourist attractions from geotagged social media data. Int. J. Digit. Earth 1–18 (2018). https://doi.org/10.1080/17538947.2018.1471104

  13. Bujari, A., Ciman, M., Gaggi, O., Palazzi, C.E.: Using gamification to discover cultural heritage locations from geo-tagged photos. Pers. Ubiquit. Comput. 21(2), 235–252 (2017). https://doi.org/10.1007/s00779-016-0989-6

    Article  Google Scholar 

  14. Cai, G., Lee, K., Lee, I.: Itinerary recommender system with semantic trajectory pattern mining from geo-tagged photos. Expert Syst. Appl. 94, 32–40 (2017). https://doi.org/10.1016/j.eswa.2017.10.049

    Article  Google Scholar 

  15. Mor, M., Dalyot, S.: Computing touristic walking routes using geotagged photographs from Flickr. ETH Zurich Research Collection, pp. 1–7 (2018). https://doi.org/10.3929/ethz-b-000225591

  16. Wu, X., Huang, Z., Peng, X.I.A., Chen, Y.: Building a spatially-embedded network of tourism hotspots from geotagged social media data. Cyber-Phys.-Soc. Comput. Netw. 6, 21945–21955 (2018). https://doi.org/10.1109/ACCESS.2018.2828032

    Article  Google Scholar 

  17. Barros, C., Moya-Gómez, B., Gutiérrez, J.: Using geotagged photographs and GPS tracks from social networks to analyse visitor behaviour in national parks. Curr. Issues Tour. 1–20 (2019). https://doi.org/10.1080/13683500.2019.1619674

  18. Sun, X., Huang, Z., Peng, X., Chen, Y., Liu, Y.: Building a model-based personalised recommendation approach for tourist attractions from geotagged social media data. Int. J. Digit. Earth 12(6), 661–678 (2019). https://doi.org/10.1080/17538947.2018.1471104

    Article  Google Scholar 

  19. Wang, H., Ouyang, W., Shen, H., Cheng, X.: ULE: learning user and location embeddings for POI recommendation. In: Proceedings of 2018 IEEE 3rd International Conference on Data Science Cyberspace, DSC 2018, pp. 99–106 (2018). https://doi.org/10.1109/dsc.2018.00023

  20. Sarwat, M., Levandoski, J.J., Eldawy, A., Mokbel, M.F.: LARS*: an efficient and scalable location-aware recommender system. IEEE Trans. Knowl. Data Eng. 26(6), 1384–1399 (2014). https://doi.org/10.1109/TKDE.2013.29

    Article  Google Scholar 

  21. Zheng, V.W., Zheng, Y., Xie, X., Yang, Q.: Towards mobile intelligence: learning from GPS history data for collaborative recommendation. Artif. Intell. 184–185, 17–37 (2012). https://doi.org/10.1016/j.artint.2012.02.002

    Article  MathSciNet  Google Scholar 

  22. Wang, H., Ouyang, W., Shen, H., Cheng, X.: ULE : learning user and location embeddings for POI recommendation. In: 2018 IEEE Third International Conference on Data Science Cyberspace, pp. 99–106 (2018). https://doi.org/10.1109/dsc.2018.00023

  23. Ding, R., Chen, Z.: RecNet: a deep neural network for personalized POI recommendation in location-based social networks. Int. J. Geogr. Inf. Sci. 32(8), 1–18 (2018). https://doi.org/10.1080/13658816.2018.1447671

    Article  Google Scholar 

  24. Guoqiong, L., Zhou, Z., Changxuan, W., Xiping, L.: POI recommendation of location-based social networks using tensor factorization. In: 2018 19th IEEE International Conference on Mobile Data Management, pp. 116–124 (2018). http://doi.ieeecomputersociety.org/10.1109/MDM.2018.00028

  25. Baral, R., Zhu, X., Iyengar, S.S., Li, T.: ReEL : review aware explanation of location recommendation. In: Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization, pp. 23–32 (2013). https://doi.org/10.1145/2509230.2509237

  26. Gao, R., et al.: Exploiting geo-social correlations to improve pairwise ranking for point-of-interest recommendation. China Commun. 15(7), 180–201 (2018). https://doi.org/10.1109/cc.2018.8424613

    Article  Google Scholar 

  27. Zhang, S., Cheng, H.: Exploiting context graph attention for POI recommendation in location-based social networks. In: Pei, J., Manolopoulos, Y., Sadiq, S., Li, J. (eds.) DASFAA 2018. LNCS, vol. 10827, pp. 83–99. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-91452-7_6

    Chapter  Google Scholar 

  28. Zhu, Q., et al.: Context-aware group recommendation for point-of-interests. IEEE Access 6, 12129–12144 (2018). https://doi.org/10.1109/ACCESS.2018.2805701

    Article  Google Scholar 

  29. Jiao, X., Xiao, Y., Zheng, W., Wang, H., Hsu, C.H.: A novel next new point-of-interest recommendation system based on simulated user travel decision-making process. Futur. Gener. Comput. Syst. 100, 982–993 (2019). https://doi.org/10.1016/j.future.2019.05.065

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to R. Sujithra @ Kanmani .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 IFIP International Federation for Information Processing

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Sujithra @ Kanmani, R., Surendiran, B. (2020). Location Based Recommender Systems (LBRS) – A Review. In: Chandrabose, A., Furbach, U., Ghosh, A., Kumar M., A. (eds) Computational Intelligence in Data Science. ICCIDS 2020. IFIP Advances in Information and Communication Technology, vol 578. Springer, Cham. https://doi.org/10.1007/978-3-030-63467-4_27

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-63467-4_27

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-63466-7

  • Online ISBN: 978-3-030-63467-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics