FCT: a fully-distributed context-aware trust model for location based service recommendation

Abstract

With the popularity of location based service (LBS), a vast number of trust models for LBS recommendation (LBSR) have been proposed. These trust models are centralized in essence, and the trusted third party may collude with malicious service providers or cause the single-point failure problem. This work improves the classic certified reputation (CR) model and proposes a novel fully-distributed context-aware trust (FCT) model for LBSR. Recommendation operations are conducted by service providers directly and the trusted third party is no longer required in our FCT model. Besides, our FCT model also supports the movements of service providers due to its self-certified characteristic. Moreover, for easing the collusion attack and value imbalance attack, we comprehensively consider four kinds of factor weights, namely number, time decay, preference and context weights. Finally, a fully-distributed service recommendation scenario is deployed, and comprehensive experiments and analysis are conducted. The results indicate that our FCT model significantly outperforms the CR model in terms of the robustness against the collusion attack and value imbalance attack, as well as the service recommendation performance in improving the successful trading rates of honest service providers and reducing the risks of trading with malicious service providers.

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

References

  1. 1

    Wang N, Shen X L, Sun Y. Transition of electronic word-of-mouth services from web to mobile context: a trust transfer perspective. Decis Support Syst, 2013, 54: 1394–1403

    Article  Google Scholar 

  2. 2

    Dhar S, Varshney U. Challenges and business models for mobile location-based services and advertising. Commun ACM, 2011, 54: 121–128

    Article  Google Scholar 

  3. 3

    Bao J, Zheng Y, Wilkie D, et al. Recommendations in location-based social networks: a survey. GeoInformatica, 2015, 19: 525–565

    Article  Google Scholar 

  4. 4

    Kuo M H, Chen L C, Liang C W. Building and evaluating a location-based service recommendation system with a preference adjustment mechanism. Expert Syst Appl, 2009, 36: 3543–3554

    Article  Google Scholar 

  5. 5

    Liu Q, Ma H, Chen E, et al. A survey of context-aware mobile recommendations. Int J Inf Tech Decis, 2013, 12: 139–172

    Article  Google Scholar 

  6. 6

    Li W, Yao M, Zhou X, et al. Recommendation of location-based services based on composite measures of trust degree. J Supercomput, 2014, 69: 1154–1165

    Article  Google Scholar 

  7. 7

    Gavalas D, Konstantopoulos C, Mastakas K, et al. Mobile recommender systems in tourism. J Netw Comput Appl, 2014, 39: 319–333

    Article  Google Scholar 

  8. 8

    Zhang T, Ma J F, Li Q, et al. Trust-based service composition in multi-domain environments under time constraint. Sci China Inf Sci, 2014, 57: 092109

    Google Scholar 

  9. 9

    Liu Z, Ma J, Jiang Z, et al. LCT: a lightweight cross-domain trust model for the mobile distributed environment. KSII Trans Internet Inf, 2016, 10: 914–934

    Google Scholar 

  10. 10

    Yu C C, Chang H. Personalized location-based recommendation services for tour planning in mobile tourism applications. In: Proceedings of the 10th International Conference on E-Commerce and Web Technologies. Berlin: Springer, 2009. 38–49

    Google Scholar 

  11. 11

    Waga K, Tabarcea A, Fränti P. Context aware recommendation of location-based data. In: Proceedings of the 15th International Conference on System Theory, Control and Computing. Piscataway: IEEE, 2011. 1–6

    Google Scholar 

  12. 12

    Barranco M J, Noguera J M, Castro J, et al. A context-aware mobile recommender system based on location and trajectory. In: Management Intelligent Systems. Berlin: Springer, 2012. 153–162

    Google Scholar 

  13. 13

    Biancalana C, Gasparetti F, Micarelli A, et al. An approach to social recommendation for context-aware mobile services. ACM Trans Intel Syst Tec, 2013, 4: 10

    Google Scholar 

  14. 14

    Yang D, Zhang D, Yu Z, et al. A sentiment-enhanced personalized location recommendation system. In: Proceedings of the 24th ACM Conference on Hypertext and Social Media. New York: ACM, 2013. 119–128

    Google Scholar 

  15. 15

    Noulas A, Scellato S, Lathia N, et al. A random walk around the city: new venue recommendation in location-based social networks. In: Proceedings of the 11th International Conference on Privacy, Security, Risk and Trust. Piscataway: IEEE, 2012. 144–153

    Google Scholar 

  16. 16

    Tan J S F, Lu E H C, Tseng V S. Preference-oriented mining techniques for location-based store search. Knowl Inf Syst, 2013, 34: 147–169

    Article  Google Scholar 

  17. 17

    Bao J, Zheng Y, Mokbel M F. Location-based and preference-aware recommendation using sparse geo-social networking data. In: Proceedings of the 20th International Conference on Advances in Geographic Information Systems. New York: ACM, 2012. 199–208

    Google Scholar 

  18. 18

    Ciaramella A, Cimino M G C A, Lazzerini B, et al. Situation-aware mobile service recommendation with fuzzy logic and semantic web. In: Proceedings of the 9th International Conference on Intelligent Systems Design and Applications. Piscataway: IEEE, 2009. 1037–1042

    Google Scholar 

  19. 19

    Bedi P, Agarwal S K. A situation-aware proactive recommender system. In: Proceedings of the 12th International Conference on Hybrid Intelligent Systems. Piscataway: IEEE, 2012. 85–89

    Google Scholar 

  20. 20

    Li K, Lin M, Lin Z, et al. Running and chasing–the competition between paid search marketing and search engine optimization. In: Proceedings of the 47th Hawaii International Conference on System Sciences. Piscataway: IEEE, 2014. 3110–3119

    Google Scholar 

  21. 21

    Guemez E. Safety system for taxi users combining reputation mechanisms and community notifications. US Patent, 13/230,632, 2011–9-12

    Google Scholar 

  22. 22

    Gambetta D, Hamill H. Streetwise: How Taxi Drivers Establish Customer’s Trustworthiness. New York: Russell Sage Foundation, 2005. 1–28

    Google Scholar 

  23. 23

    Balafoutas L, Beck A, Kerschbamer R, et al. What drives taxi drivers? a field experiment on fraud in a market for credence goods. Rev Economic Studies, 2013, 80: 876–891

    Article  Google Scholar 

  24. 24

    Huynh T D, Jennings N R, Shadbolt N R. Certified reputation: how an agent can trust a stranger. In: Proceedings of the 5th International Joint Conference on Autonomous Agents and Multiagent Systems. New York: ACM, 2006. 1217–1224

    Google Scholar 

  25. 25

    Kerr R, Cohen R. Modeling trust using transactional, numerical units. In: Proceedings of the 2006 International Conference on Privacy, Security and Trust: Bridge the Gap Between PST Technologies and Business Services. New York: ACM, 2006. 21

    Google Scholar 

  26. 26

    Solanas A, Mart´ınez-Ballesté A. A TTP-free protocol for location privacy in location-based services. Comput Commun, 2008, 31: 1181–1191

    Article  Google Scholar 

  27. 27

    Rao U P, Girme H. A novel framework for privacy preserving in location based services. In: Proceedings of the 15th International Conference on Advanced Computing and Communication Technologies. Piscataway: IEEE, 2015. 272–277

    Google Scholar 

  28. 28

    Doppler K, Rinne M P, Janis P, et al. Device-to-device communications; functional prospects for LTE-advanced networks. In: Proceedings of the 2009 IEEE International Conference on Communications Workshops. Piscataway: IEEE, 2009: 1–6

    Google Scholar 

  29. 29

    Condoluci M, Militano L, Orsino A, et al. LTE-direct vs. WiFi-direct for machine-type communications over LTEA systems. In: Proceedings of the 26th Annual International Symposium on Personal, Indoor and Mobile Radio Communications. Piscataway: IEEE, 2015: 2298–2302

    Google Scholar 

  30. 30

    Dorigo M, Gambardella L M. Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans Evolut Comput, 1997, 1: 53–66

    Article  Google Scholar 

  31. 31

    Liu Z, Ma J, Jiang Z, et al. LSOT: a lightweight self-organized trust model in VANETs. Mob Inf Syst, 2016, 2016: 7628231

    Google Scholar 

  32. 32

    Nguyen H T, Zhao W, Yang J. A trust and reputation model based on bayesian network for web services. In: Proceedings of the 2010 IEEE International Conference on Web Services. Piscataway: IEEE, 2010. 251–258

    Google Scholar 

  33. 33

    Zhang H, Wang Y, Zhang X. Transaction similarity-based contextual trust evaluation in e-commerce and e-service environments. In: Proceedings of the 2011 IEEE International Conference on Web Services. Piscataway: IEEE, 2011. 500–507

    Google Scholar 

  34. 34

    Liu G, Liu A, Wang Y, et al. An efficient multiple trust paths finding algorithm for trustworthy service provider selection in real-time online social network environments. In: Proceedings of the 2014 IEEE International Conference on Web Services. Piscataway: IEEE, 2014. 121–128

    Google Scholar 

  35. 35

    Liu Z, Ma J, Jiang Z, et al. IRLT: integrating reputation and local trust for trustworthy service recommendation in service-oriented social networks. Plos One, 2016, 11: e0151438

    Article  Google Scholar 

  36. 36

    Sun Y L, Han Z, Yu W, et al. A trust evaluation framework in distributed networks: vulnerability analysis and defense against attacks. In: Proceedings of the 2006 IEEE International Conference on Computer Communications. Piscataway: IEEE, 2006. 1–13

    Google Scholar 

Download references

Acknowledgements

This work was supported by National High Technology Research and Development Program (863 Program) (Grant No. 2015AA016007), National Natural Science Foundation of China (Grant No. 61502375, 61370078), Key Program of NSFC (Grant No. U1405255), and Natural Science Basis Research Plan in Shaanxi Province of China (Grant No. 2016JQ6046).

Author information

Affiliations

Authors

Corresponding author

Correspondence to Jianfeng Ma.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Liu, Z., Ma, J., Jiang, Z. et al. FCT: a fully-distributed context-aware trust model for location based service recommendation. Sci. China Inf. Sci. 60, 082102 (2017). https://doi.org/10.1007/s11432-015-9029-y

Download citation

Keywords

  • trust model
  • fully-distributed
  • context-aware
  • location based service
  • service recommendation