Abstract
Because of the anonymity and openness of online transactions and the richness of network resources, the problems of the credibility of the online trading and the exact selection of network resources have become acute. For this reason, a reputation-based multi-agent model for network resource selection (RMNRS) is presented. The model divides the network into numbers of trust domains. Each domain has one domain-agent and several entity-agents. The model prevents the inconsistency of information that is maintained by differ-ent agents through the periodically communication between the agents. The model enables the consumers to receive responses from agents significantly quicker than that of traditional models, because the global reputation values of service providers and consumers are evaluated and updated dynamically after each transaction. And the model allocates two global reputation values to each entity and takes the recognition value that how much the service provider knows the service into account. In order to make users choose the best matching services and give users with trusted services, the model also takes the similarity between services into account and uses the similarity degree to amend the integration reputation value with harmonic-mean. Finally, the effectiveness and feasibility of this model is illustrated by the experiment.
Highlights
The World Wide Web has evolved at an extreme rate due to its capacity to provide an endless amount of resources to the public users
In order to make users choose the best matching services and give users with trusted services, the model takes the similarity between services into account and uses the similarity degree to amend the integration reputation value with harmonic-mean
In this paper we present a reputation-based multi-agent model for network resource selection (RMNRS) which prevents the inconsistency of information maintained by different agents through the periodically communication between the agents
Summary
The World Wide Web has evolved at an extreme rate due to its capacity to provide an endless amount of resources to the public users. Recommender system which is based on trust have proven to be an important method to effectively find those resources that users are interested in from endless resources in the network, by providing users with more proactive and personalized information services. The exiting recommender systems based on trust filtered recommendation information just through authenticating users’ identity or removing users with low trust value [5]. They didn’t consider the following four problems: 1) the role of transaction behavior for users in e-commerce has two types: buyer and seller, so we can’t only use one trust value or reputation value to measure the users’ trust level with different transaction behavior. [8]; 4) after each transaction, both the participators should update their trust values or reputation values and should share their trust information of transactions which can increase the spread of trust information and raise the performance of network
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