  Recently, textual information has been proved to play a positive role in
recommendation systems. However, most of the existing methods only focus on
representation learning of textual information in ratings, while potential
selection bias induced by the textual information is ignored. In this work, we
propose a novel and general self-adaptive module, the Self-adaptive Attention
Module (SAM), which adjusts the selection bias by capturing contextual
information based on its representation. This module can be embedded into
recommendation systems that contain learning components of contextual
information. Experimental results on three real-world datasets demonstrate the
effectiveness of our proposal, and the state-of-the-art models with SAM
significantly outperform the original ones.
