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.
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References
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
Dhar S, Varshney U. Challenges and business models for mobile location-based services and advertising. Commun ACM, 2011, 54: 121–128
Bao J, Zheng Y, Wilkie D, et al. Recommendations in location-based social networks: a survey. GeoInformatica, 2015, 19: 525–565
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
Liu Q, Ma H, Chen E, et al. A survey of context-aware mobile recommendations. Int J Inf Tech Decis, 2013, 12: 139–172
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
Gavalas D, Konstantopoulos C, Mastakas K, et al. Mobile recommender systems in tourism. J Netw Comput Appl, 2014, 39: 319–333
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
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
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
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
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
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
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
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
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
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
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
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
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
Guemez E. Safety system for taxi users combining reputation mechanisms and community notifications. US Patent, 13/230,632, 2011–9-12
Gambetta D, Hamill H. Streetwise: How Taxi Drivers Establish Customer’s Trustworthiness. New York: Russell Sage Foundation, 2005. 1–28
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
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
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
Solanas A, Mart´ınez-Ballesté A. A TTP-free protocol for location privacy in location-based services. Comput Commun, 2008, 31: 1181–1191
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
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
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
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
Liu Z, Ma J, Jiang Z, et al. LSOT: a lightweight self-organized trust model in VANETs. Mob Inf Syst, 2016, 2016: 7628231
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
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
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
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
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
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).
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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
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DOI: https://doi.org/10.1007/s11432-015-9029-y