Many trust-aware recommendation systems have emerged to overcome the problem of data sparsity, which bottlenecks the performance of traditional Collaborative Filtering (CF) recommendation algorithms. However, these systems most rely on the binary social network information, failing to consider the variety of trust values between users. To make up for the defect, this paper designs a novel Top-N recommendation model based on trust and social influence, in which the most influential users are determined by the Improved Structural Holes (ISH) method. Specifically, the features in Matrix Factorization (MF) were configured by deep learning rather than random initialization, which has a negative impact on prediction of item rating. In addition, a trust measurement model was created to quantify the strength of implicit trust. The experimental result shows that our approach can solve the adverse impacts of data sparsity and enhance the recommendation accuracy.

中文翻译：

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Top-N推荐的具有信任和社会影响力的因子相似模型

已经出现了许多可信任的推荐系统，以克服数据稀疏性的问题，该问题限制了传统的协同过滤（CF）推荐算法的性能。但是，这些系统大多数依赖于二进制社交网络信息，无法考虑用户之间的信任值的多样性。为了弥补该缺陷，本文设计了一种基于信任和社会影响力的新型Top-N推荐模型，其中，最有影响力的用户是通过改良结构漏洞（ISH）方法确定的。具体来说，矩阵分解（MF）中的功能是通过深度学习而不是随机初始化来配置的，这对项目评级的预测具有负面影响。此外，创建了信任度量模型以量化隐式信任的强度。