Integrating social networks as auxiliary information shows effectiveness in improving the performance for a recommendation task. Typical models usually characterize the user trust relationship as a binary adjacent matrix derived from a social graph, which basically only incorporates neighborhood interactions, and then encodes the trust values of different individuals with the same value. Such methods fail to capture the implicit high-order relations hidden under a graph structure, and thereby ignore the impact of indirect influencers. To address the aforementioned problems, we present an I mplicit T rust R elation-A ware model (ITRA) based on Variational Auto-Encoder (VAE). ITRA adopts an attention module to feed the weighted trust embedding information into an inherited non-linear VAE structure. In this sense, ITRA could provide recommendations by reconstructing a non-binary adjacency social matrix with implicit high-order interactions from both indirect key opinion leaders and explicit connections from neighbors. The extensive experiments conducted on three datasets illustrate that ITRA could achieve a better performance compared to the state-of-the-art methods.

将社交网络集成为辅助信息显示了提高推荐任务性能的有效性。典型的模型通常将用户信任关系刻画为从社交图导出的二元邻域矩阵，基本只包含邻域交互，然后将不同个体的信任值编码为相同值。这种方法无法捕捉隐藏在图结构下的隐含高阶关系，从而忽略了间接影响者的影响。为了解决上述问题，我们提出了一个予mplicit Ť锈ř elation-甲件模型（ITRA）基于变分自动编码器（VAE）。ITRA 采用注意力模块将加权信任嵌入信息馈送到继承的非线性 VAE 结构中。从这个意义上说，ITRA 可以通过重建具有来自间接关键意见领袖和来自邻居的显式联系的隐式高阶交互的非二元邻接社会矩阵来提供建议。在三个数据集上进行的大量实验表明，与最先进的方法相比，ITRA 可以实现更好的性能。