Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

A location history-aware recommender system for smart retail environments

Abstract

Recommender systems (RSs) represent integral parts of e-commerce platforms for almost two decades now. The recent emergence of mobile context-aware RSs (CARS) contributed in improving the relevance of recommendations derived by “traditional” RSs through adapting them to the situational user context. This article presents the design and implementation aspects of a collaborative filtering-based mobile CARS, which has been integrated in a smart retailing platform that enables location-based search for retail products and services. In addition to user location, the introduced CARS considers several context parameters like time, season, demographic data, consumer behavior, and location history of the user in order to derive more meaningful product recommendations. Our RS has undergone field trials as well as formal laboratory evaluation tests demonstrating higher accuracy and relevance of recommendations compared with two baseline approaches.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Notes

  1. 1.

    http://www.smartbuy.tech/

  2. 2.

    Rand index or Rand measure, in statistics, is a measure of the similarity between two data clustering.

  3. 3.

    https://play.google.com/store/apps/details?id=com.smartbuyshopping.app

References

  1. 1.

    Adomavicius G, Tuzhilin A (2005) Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans Knowl Data Eng 17(6):734–749

  2. 2.

    Adomavicius G, Tuzhilin A (2011) Context-aware recommender systems. In Recommender systems handbook (pp. 217-253). Springer, Boston, MA. https://doi.org/10.1007/978-0-387-85820-3_7

  3. 3.

    Bourg L et al. (2019) Enhanced buying experiences in smart cities: the SMARTBUY approach. Proceedings of the 2019 European Conference on Ambient Intelligence (AmI’2019): 108-122

  4. 4.

    Breese JS, Heckerman D, Kadie C (1998) Empirical analysis of predictive algorithms for collaborative filtering. Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence (UAI’98): 43–52

  5. 5.

    Chatzidimitris T et al. (2019) A location history-aware retail product recommender system. Proceedings of the 2019 International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob’2019): 1-6

  6. 6.

    Dorotic M, Fok D, Verhoef PC, Bijmolt TH (2011) Do vendors benefit from promotions in a multivendor loyalty program? Mark Lett 22(4):341–356

  7. 7.

    Gavalas D, Kenteris M (2011) A web-based pervasive recommendation system for mobile tourist guides. Pers Ubiquit Comput 15(7):759–770

  8. 8.

    Gavalas D, Konstantopoulos C, Mastakas K, Pantziou G (2014) Mobile recommender systems in tourism. J Netw Comput Appl 39:319–333

  9. 9.

    Gavalas D, Kasapakis V, Konstantopoulos C, Pantziou G, Vathis N (2017) Scenic route planning for tourists. Pers Ubiquit Comput 21(1):137–155

  10. 10.

    Gunawardana A, Shani G (2015) Evaluating recommender systems. In: Recommender Systems Handbook. Springer, Boston, pp 265–308

  11. 11.

    Herlocker JL, Konstan JA, Terveen LG, Riedl JT (2004) Evaluating collaborative filtering recommender systems. ACM Trans Inf Syst 22(1):5–53

  12. 12.

    Herzog D, Laß C, Wörndl W (2018) Tourrec: a tourist trip recommender system for individuals and groups. Proceedings of the 12th ACM Conference on Recommender Systems (RecSys’2018): 496-497

  13. 13.

    Horozov T, Narasimhan N, Vasudevan V (2006) Using location for personalized POI recommendations in mobile environments. Proceedings of the 2006 International Symposium on Applications and the Internet (SAINT’2006): 124-129

  14. 14.

    Kotkov D, Wang S, Veijalainen J (2016) A survey of serendipity in recommender systems. Knowl-Based Syst 111:180–192

  15. 15.

    Lawrence RD, Almasi GS, Kotlyar V, Viveros M, Duri SS (2001) Personalization of supermarket product recommendations. In Applications of data mining to electronic commerce (pp. 11-32). Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-1627-9_2

  16. 16.

    Li YM, Chou CL, Lin LF (2014) A social recommender mechanism for location-based group commerce. Inf Sci 274:125–142

  17. 17.

    Lu J, Wu D, Mao M, Wang W, Zhang G (2015) Recommender system application developments: a survey. Decis Support Syst 74:12–32

  18. 18.

    Papagelis M, Plexousakis D (2005) Qualitative analysis of user-based and item-based prediction algorithms for recommendation agents. Eng Appl Artif Intell 18(7):781–789

  19. 19.

    Rand WM (1971) Objective criteria for the evaluation of clustering methods. J Am Stat Assoc 66(336):846–850

  20. 20.

    Ricci F (2011) Mobile recommender systems. International Journal of Information Technology and Tourism 12(3):205–231

  21. 21.

    Ricci F, Rokach L, Shapira B (2011) Introduction to recommender systems handbook. In Recommender systems handbook (pp. 1-35). Springer, Boston, MA. https://doi.org/10.1007/978-0-387-85820-3_1

  22. 22.

    Shi Y, Serdyukov P, Hanjalic A, Larson M (2011) Personalized landmark recommendation based on geotags from photo sharing sites. Proceedings of the 5th International AAAI Conference on Web and Social Media, 622–625

  23. 23.

    Xiao X, Zheng Y, Luo Q, Xie X (2014) Inferring social ties between users with human location history. J Ambient Intell Humaniz Comput 5(1):3–19

  24. 24.

    Yang W-S, Cheng HC, Dia JB (2008) A location-aware recommender system for mobile shopping environments. Expert Syst Appl 34(1):437–445

  25. 25.

    Yu X, Pan A, Tang LA, Li Z, Han J (2011) Geo-friends recommendation in GPS-based cyber-physical social network. International Conference on Advances in Social Networks Analysis and Mining (ASONAM’2011), 361–368

  26. 26.

    Yuan ST, Tsao YW (2003) A recommendation mechanism for contextualized mobile advertising. Expert Syst Appl 24(4):399–414

Download references

Funding

This research has been co-financed by the European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH–CREATE–INNOVATE (project code: T1EDK-01572). V. Kasapakis, G. Pantziou and C. Zaroliagis have been partially supported by the EU H2020 Programme under grant agreement no. 687960 (SMARTBUY).

Author information

Correspondence to Damianos Gavalas.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Chatzidimitris, T., Gavalas, D., Kasapakis, V. et al. A location history-aware recommender system for smart retail environments. Pers Ubiquit Comput (2020). https://doi.org/10.1007/s00779-020-01374-7

Download citation

Keywords

  • Recommender system
  • Collaborative filtering
  • E-commerce
  • M-commerce
  • Retailer shop
  • Shopping mall
  • Smart retailing
  • Location-based search
  • Context awareness
  • Location history