基于社交网络信任关系的服务推荐方法

Service Recommendation Method Based on Social Network Trust Relationships

随着服务型计算的兴起,大量跨领域电子服务应运而生。用户要从众多服务中挑选出适合自己且可信的服务十分困难,因而提出高效的服务推荐算法十分必要。传统的协同推荐方法存在冷启动、数据稀疏以及实时性不好等问题,在评分数据较少时推荐效果不佳。为获得更好的推荐结果,文中在社交网络中使用信任传递机制,建立信任传递模型,由此获取任意用户间的信任度。另一方面,设计了相似性判定指标,凭借系统评分数据,求得用户间的偏好相似度。在得到用户间信任度和偏好相似度的基础上,根据社交网络的特性,动态结合两部分指标以获得综合推荐权重,再以此权重替代传统相似度衡量标准进行基于用户的协同过滤推荐。所提方法能在解决传统推荐算法问题的基础上进一步提升推荐效果,并以准确率、覆盖率为标准在Epinions数据集上进行验证,获得了较好的效果。

With the advent of service computing,many different electronic services have emerged.Users often have to find what they need from a large number of services,which is a formidable task.Hence,it is necessary to put forward an efficient recommendation algorithm.The traditional cooperative recommendation system has some problems,such as cold start,sparsity of data and poor real-time performance,which lead to poor recommendation results under the circumstances with less scoring data.In order to get a better recommendation result,this paper introduced trust transfer in social networks and utilized it to establish a trust transfer model to obtain trust among users.On the other hand,based on the score data,the similarity between users in the system is calculated.On the basis of similarity between users’trust and preference,according to the characteristics of social networks,users’trust and preference are dynamically combined to obtain comprehensive recommendation weights.The comprehensive recommendation weights can replace the traditional similarity measurement standards for user-based collaborative filtering recommendation.This method was verified through the Epinions data set and can further improve the recommendation effect and.