To improve recommendation quality, the existing trust-based recommendation methods often directly use the binary trust relationship of social networks, and rarely consider the difference and potential influence of trust strength among users. To make up for the gap, this paper puts forward a hybrid top-N recommendation algorithm that combines mutual trust and influence. Firstly, a new trust measurement method was developed based on dynamic weight, considering the difference of trust strength between users. Secondly, a new mutual influence measurement model was designed based on trust relationship, in light of the social network topology. Finally, two hybrid recommendation algorithms, denoted as FSTA(Factored Similarity model with Trust Approach) and FSTI(Factored similarity models with trust and influence), were presented to solve the data sparsity and binarity. The two algorithms integrate user similarity, item similarity, mutual trust and mutual influence. Our approach was compared with several other recommendation algorithms on three standard datasets: FilmTrust, Epinions and Ciao. The experimental results proved the high efficiency of our approach.

中文翻译：

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基于互信与影响的Top-N推荐

为了提高推荐质量，现有的基于信任的推荐方法经常直接使用社交网络的二元信任关系，很少考虑用户之间信任强度的差异和潜在影响。为了弥补这一差距，本文提出了一种将互信与影响相结合的top-N混合推荐算法。首先，考虑到用户之间信任强度的差异，提出了一种基于动态权重的信任度量方法。其次，根据社交网络拓扑结构，设计了一种基于信任关系的新的相互影响度量模型。最后，两种混合推荐算法，分别表示为FSTA（信任方法的因子相似度模型）和FSTI（信任和影响的因子相似度模型），提出解决数据稀疏性和二值性。这两种算法整合了用户相似性，项目相似性，相互信任和相互影响。我们的方法与三个标准数据集上的其他几种推荐算法进行了比较：FilmTrust，Epinions和Ciao。实验结果证明了我们方法的高效率。