Combining Global and Local Trust for Service Recommendation
Recommending trusted services to users is of paramount value in service-oriented environments. Reputation has been widely used to measure the trustworthiness of services, and various reputation models for service recommendation have been proposed. Reputation is basically a global trust score obtained by aggregating trust from a community of users, which could be conflicting with an individual's personal opinion on the service. Evaluating a service's trustworthiness locally based on the evaluating user's own or his/her friends' experiences is sometimes more accurate. However, local trust assessment may fail to work when no trust path from an evaluating user to a target service exists. This paper proposes a hybrid trust-aware service recommendation method for service-oriented environment with social networks via combining global trust and local trust evaluation. A global trust metric and a local trust metric are firstly presented, and then a strategy for combining them to predict the final trust of service is proposed. To evaluate the proposed method's performance, we conducted several simulations based on a synthesized dataset. The simulation results show that our proposed method outperforms the other methods in service recommendation.
M. Tang et al., "Combining Global and Local Trust for Service Recommendation," Proceedings of the 11th IEEE International Conference on Services Computing (2014, Anchorage, AK), pp. 305-312, Institute of Electrical and Electronics Engineers (IEEE), Jun 2014.
The definitive version is available at https://doi.org/10.1109/ICWS.2014.52
2014 21st IEEE International Conference on Web Services (2014: Jun. 27-Jul. 2, Anchorage, AK)
Keywords and Phrases
Information services; Social networking (online); Reputation; Reputation models; Service recommendations; Service-oriented environment; Target services; Trust; Trust assessments; Trust evaluation; Web services
International Standard Book Number (ISBN)
Article - Conference proceedings
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