Recommender systems (RS), which have been an essential part in a wide range of applications, can be formulated as a matrix completion (MC) problem. To boost the performance of MC, matrix completion with side information, called inductive matrix completion (IMC), was further proposed. In real applications, the factorized version of IMC is more favored due to its efficiency of optimization and implementation. Regarding the factorized version, traditional IMC method can be interpreted as learning an individual representation for each feature, which is independent from each other. Moreover, representations for the same features are shared across all users/items. However, the independent characteristic for features and shared characteristic for the same features across all users/items may limit the expressiveness of the model. The limitation also exists in variants of IMC, such as deep learning based IMC models. To break the limitation, we generalize recent advances of self-attention mechanism to IMC and propose a context-aware model called collaborative self-attention (CSA), which can jointly learn context-aware representations for features and perform inductive matrix completion process. Extensive experiments on three large-scale datasets from real RS applications demonstrate effectiveness of CSA.

翻译：建议系统(RS)是广泛应用中一个必不可少的组成部分,可以作为矩阵完成问题(MC)来拟订。为了提高MC的性能,还进一步提议了矩阵完成和侧面信息,称为感应矩阵完成(IMC),在实际应用中,IMC的分数版因其优化和实施效率而更受偏好。关于分保制版本,传统的IMC方法可被解释为学习每个特征的单个代表,这种代表是相互独立的。此外,所有用户/项目都共享相同特征的表述。但是,所有用户/项目具有相同特征的独立特征和共同特征的特征可能会限制模型的清晰度。在IMC的变式中也存在局限性,例如基于深度学习的IMC模型。为了打破这一局限性,我们将自留机制的最新进展推广到IMC,并提议一个称为协作自留模式的背景意识模式,即所有用户/项目都可共同学习对特征的背景表示并进行缩矩阵完成过程。从实际的RSSA应用中对三个大型数据集进行广泛的实验。