Skip to main content
SpringerLink
Log in

Research on Context-Awareness Mobile Tourism E-Commerce Personalized Recommendation Model

  • Published:
Journal of Signal Processing Systems Aims and scope Submit manuscript

Abstract

E-commerce personalized recommendation problem in social network based on context is of great realistic significance to users and merchants. Aiming at data sparsity and low precision of personalized recommendation in tourism E-commerce personalized recommendation model integrating multivariate social information, this paper integrates social information such as trust relationship between users, time and geographic position of commodity purchasing into traditional collaborative filtering recommendation mode based on users and proposed context-awareness mobile tourism E-commerce personalized recommendation model-MTERec, which digs interest and preference of users under different contexts, calculates weight of user interest from perspective of mobile environment context where the user is located and finally refers to idea recommended by collaborative filtering recommendation to realize rating prediction of users to commodities and recommend according to interest and preference of the users. Experimental results indicate that compared with existing similar algorithms, context- awareness mobile tourism E-commerce personalized recommendation model and algorithm proposed in this paper have higher recommendation precision and user’s satisfaction degree.

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

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5

Similar content being viewed by others

References

  1. Bao, J., Zheng, Y., Wilkie, D., et al. (2015). Recommendations in location-based social networks: A survey[J]. Geoinformatica, 19(3), 525–565.

    Article  Google Scholar 

  2. Zheng, Y. (2012) Tutorial on location-based social networks[C]//proceedings of international conference on world wide web.

  3. Gu, H. S., Xie, X., Lv, Q., Ruan, Y. P., Shang, L. E. (2011). Effective and efficient event modeling for real-time online social media networks.//Proc of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology, Volume 01. Washington, DC, USA. 300–307.

  4. Gu, H. S., Gartrell, M., Zhang, L., Lv, Q., Grunwald, D., Anchor, M. F. (2013) Towards effective event context identification.//Proc of the 22nd ACM international conference on Conference on information & knowledge management. Burlingame, CA, USA. 629–638.

  5. Fang, X. K. (2015). A new method of social information recommendation by combining contextual factors [J]. Library and Information Service, 59(22), 105–111,129.

    Google Scholar 

  6. Meng, X. W., Hu, X., Wang, L. C., & Zhang, Y. J. (2013). Mobile recommender systems and their applications. Journal of Software, 24(1), 91–108.

    Article  MathSciNet  Google Scholar 

  7. Gong, Y., Li, Y., Jin, D., et al. (2011) A location prediction scheme based on social correlation[C] //vehicular technology conference (VTC spring), 2011 IEEE 73rd. IEEE, 1–5.

  8. Thanh, N., Phuong, T. M. (2007). A gaussian mixture model for mobile location prediction. //Proc of the 9th International Conference on Advanced Communication Technology, 2(9): 152–157.

  9. Dong, F., Luo, J., Zhu, X., et al. (2013) A personalized hybrid recommendation system oriented to e-commerce mass data in the cloud[C]//2013 IEEE international conference on systems, man, and cybernetics. IEEE, 1020–1025.

  10. Fang, Y., Qureshi, I., Sun, H., et al. (2014). Trust, satisfaction, and online repurchase intention: The moderating role of perceived effectiveness of E-commerce institutional mechanisms[J]. MIS Quarterly, 38(2), 407–427.

    Article  Google Scholar 

  11. Xu, H., Zhang, R., Lin, C., et al. (2014). Construction of E-commerce recommendation system based on semantic annotation of ontology and user preference[J]. Indonesian Journal of Electrical Engineering and Computer Science, 12(3), 2028–2035.

    Google Scholar 

  12. Gavalas, D., Konstantopoulos, C., Mastakas, K., et al. (2014). Mobile recommender systems in tourism[J]. Journal of Network and Computer Applications, 39, 319–333.

    Article  Google Scholar 

  13. Grbovic, M., Radosavljevic, V., Djuric, N. et al. (2015) E-commerce in your inbox: Product recommendations at scale[C]//proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, 1809–1818.

  14. Zhang, Z., & Liu, H. (2015). Social recommendation model combining trust propagation and sequential behaviors[J]. Applied Intelligence, 43(3), 695–706.

    Article  Google Scholar 

  15. Zhang, Z. J., & Liu, H. (2015). Research on context-awareness mobile SNS recommendation algorithm[J]. Pattern Recognition and Artificial Intelligence, 28(5), 404–410.

    Google Scholar 

  16. Zhao, W. X., Li, S., He, Y., et al. (2016). Connecting social media to E-commerce: Cold-start product recommendation using microblogging information[J]. IEEE Transactions on Knowledge and Data Engineering, 28(5), 1147–1159.

    Article  Google Scholar 

  17. Lucas, J. P., & Luz, N. (2013). Moreno MN.A hybrid recommendation approach for a tourism system[J]. Expert Systems with Applications, 40(9), 3532–3550.

    Article  Google Scholar 

  18. Zhong, H. D., Zhang, S., Wang, Y., & Shu, Y. (2014). Study on directed trust graph based recommendation for E-commerce system [J]. International Journal of Computers Communications & Control, 9(4), 510–523.

    Article  Google Scholar 

  19. Sigala, M. (2017). Collaborative commerce in tourism: implications for research and industry[J]. Current Issues in Tourism, 20(4), 346–355.

    Article  Google Scholar 

  20. Li, J., Qiu, M., Niu, J. et al. 2010). Feedback Dynamic Algorithms for Preemptable Job Scheduling in Cloud Systems. IEEE International Conference on Web Intelligence and Intelligent Agent Technology, 561–564.

  21. Wang, L. C., Meng, X. W., & Zhang, Y. J. (2012). Context-aware recommender systems. Journal of Software, 23(1), 1–20.

    Article  Google Scholar 

  22. Fu, M. (2012) A research on trust and distrust-based collaborative filtering recommendation model [D]Qinhuangdao: Yanshan University.

  23. Gao, H., Tang, J., Liu, H. (2012). Mobile location prediction in spatio-temporal context.//Proc of the Mobile Data Challenge by Nokia Workshop in conjunction with International Conference on Pervasive Computing. Newcastle, UK, 32–39.

Download references

Acknowledgments

This paper is made possible thanks to the generous support from the National Natural Science Foundation of China (61503220), Natural Science Foundation of Shandong Province (ZR2016FM19), Key Research and Development Program of Shandong Province (2018GGX106006, 2019GGX101068), Jinan Science and Technology Project (201704065), A Project of Shandong Province Higher Educational Science and Technology Program (J17KA070), Doctoral Foundation of Shandong Jianzhu University (XNBS1523).

Author information

Authors and Affiliations

  1. Business School, Shandong Normal University, Jinan, 250014, China

    Huali Pan

  2. School of Computer Science and Technology, Shandong Jianzhu University, Jinan, 250101, China

    Zhijun Zhang

Authors
  1. Huali Pan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhijun Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhijun Zhang.

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

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pan, H., Zhang, Z. Research on Context-Awareness Mobile Tourism E-Commerce Personalized Recommendation Model. J Sign Process Syst 93, 147–154 (2021). https://doi.org/10.1007/s11265-019-01504-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11265-019-01504-2

Keywords

Search

Navigation