  Recommender systems play a fundamental role in web applications in filtering
massive information and matching user interests. While many efforts have been
devoted to developing more effective models in various scenarios, the
exploration on the explainability of recommender systems is running behind.
Explanations could help improve user experience and discover system defects. In
this paper, after formally introducing the elements that are related to model
explainability, we propose a novel explainable recommendation model through
improving the transparency of the representation learning process.
Specifically, to overcome the representation entangling problem in traditional
models, we revise traditional graph convolution to discriminate information
from different layers. Also, each representation vector is factorized into
several segments, where each segment relates to one semantic aspect in data.
Different from previous work, in our model, factor discovery and representation
learning are simultaneously conducted, and we are able to handle extra
attribute information and knowledge. In this way, the proposed model can learn
interpretable and meaningful representations for users and items. Unlike
traditional methods that need to make a trade-off between explainability and
effectiveness, the performance of our proposed explainable model is not
negatively affected after considering explainability. Finally, comprehensive
experiments are conducted to validate the performance of our model as well as
explanation faithfulness.
