To ease cold start and data sparsity problems in web service recommendation

and satisfy user’s personalized requirements

we propose a web service recommendation framework and algorithms based on heterogeneous social network. Web service provider

as an element in the network

could provide more real information to help relieve cold start. Based on the proposed recommendation framework

we design CTV (Computing Trust Value) algorithm to predict trust value of user to candidate service and RQ (Recommend Queue) algorithm to recommend services. At last

we conduct a series of experiments based on real data set. The results indicate that the proposed method outperforms the existing methods at the metrics of MAE

RMSE for predictive accuracy

MAP

NDCG for recommendation accuracy

and filling ratio and coverage ratio

and it is helpful to improve personalized recommendation performance.