A new personalized web service recommendation method

To deal with users´ individualized requirements of traditional service discovery, this paper proposed a web service recommendation method based on context clustering. Firstly, it built the context model for describing user and service information. Secondly, it introduced service cache mechanism and used fuzzy C-means clustering algorithm to achieve initial screening of service, which is based on the function and quality of service. Then it exploited the service clustering, and combined user character with user evaluation to cluster user with similarity context. Preliminary result has been optimized, thus it provided personalized services for user. The experiment shows that the proposed method is feasibility, and better than other methods in the accuracy and time efficiency of service recommendation.