基于混杂社会网络的个性化Web服务推荐方法

为了缓解Web服务推荐中存在的冷启动和数据稀疏问题，以及满足用户个性化的需求，本文提出了基于混杂社会网络的Web服务推荐框架及算法.该网络加入了服务提供者这一元素，可提供更多的真实信息，有助于缓解冷启动问题.根据提出的服务推荐框架，设计了用户-候选服务信任值预测算法（Computing Trust Value，CTV），以及服务推荐算法（Recommend Queue，RQ）.在真实数据集上建立实验，结果表明本文提出的方法在预测准确率MAE、RMSE，推荐准确率MAP、NDCG，以及填充率和覆盖率上都有所提高，有助于提升个性化推荐的性能.

Personalized Web Service Recommendation Based on Heterogeneous Social Network

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.