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Semantic web service discovery based on user preference cluster

In the converged network environment, owing to diversification of user demand and diversification of services, the existing semantic service discovery algorithms almost have the problem of too long service response time. With respect to this current situation, this paper proposes a novel services semantic web service discovery method based on user preference cluster. Firstly, this paper optimizes the design of Unmixed Semantic UDDI Model and then makes use of the clustering technology to preprocess the user preference from the standpoint of user demand before service discovery. For that reason, a user could receive demanded services quickly, which have been thought highly of other users, whose preferences similar to, reducing processing semantic information. Verified by actual test environment, this method can shorten the service discovery response time on condition of not affecting the service discovery accuracy, thereby enhancing the performance of Unmixed Semantic UDDI.