  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.
