基于社会网络分析的协同推荐方法改进

Collaborative recommendation method improvement based on social network analysis

协同推荐是电子商务中被广泛使用的个性化服务技术,但由于数据稀疏、冷启动等原因,导致现有协同推荐方法的个性化服务水平不高。为提高协同推荐的准确性,利用社会网络分析对协同推荐方法加以改进,提出一种基于社会网络分析改进的协同推荐方法。该方法利用社会网络分析技术分析用户间的关系,将其量化为信任度以填充用户-项矩阵,并将信任度融入到用户相似性计算中。通过实验分析验证了所提方法的有效性。以信任度扩充用户-项矩阵不仅可以较好地解决协同推荐中数据稀疏和冷启动问题,而且能够提高协同推荐的准确性。

Collaborative recommendation is widely used in E-commerce personalized service. But the existing methods cannot provide high level personalized service due to sparse data and cold start. To improve the accuracy of collaborative recommendation, a collaborative recommendation method based on Social Network Analysis （SNA） was proposed in this paper by using SNA to improve the collaborative recommendation methods. The proposed method used SNA technology to analyze the trust relationships between users, then quantified the relationships as trust values to fill the user-item matrix, and used these trust values to calculate the similarity of users. The effectiveness of the proposed method was verified by the experimental analysis. Using trust values to expand the user-item matrix can not only solve the problem of sparse data and cold start effectively, but also improve the accuracy of collaborative recommendation.