融合社会标签与信任关系的社会网络推荐方法

针对推荐系统中普遍存在的数据稀疏和冷启动等问题，本文将标签与基于信任的社交推荐方法相结合，提出了一种融合社会标签和信任关系的社会网络推荐方法。该方法利用概率因式分解技术实现了社会信任关系、项目标记信息和用户项目评分矩阵的集成。从不同维度出发，实现了用户和项目潜在特性空间的互连。在此基础上，通过概率矩阵因式分解技术实现降维，从而实现了有效的社会化推荐。在Epinions和Movielens数据集上的实验结果表明本文所提出的方法优于传统的社会化推荐和社会标签推荐算法，特别是当用户评分数据较少时该算法的优越性体现得更好。

Social Networks Recommendation Based on Social Tag and Trust Relation

Aiming at the universal problem of data sparsity and cold start in recommendation system, a social network recommendation method based on the combination of social label and trust relation is proposed in the paper. The proposed method uses the probabilistic factorization technique to integrate the social trust, item marking information and user item score matrix. The interconnection of users and potential item feature space is realized from different dimensions. On this basis, the realization of dimension reduction by the factorization of probability matrix can achieve effective social recommendation. Experimental results on Epinions and Movielens data sets show that the proposed method is superior to traditional social recommendation and social label recommendation algorithms, especially in the case of less user score data.