  Disentangled representation has been widely explored in many fields due to
its maximal compactness, interpretability and versatility. Recommendation
system also needs disentanglement to make representation more explainable and
general for downstream tasks. However, some challenges slow its broader
application -- the lack of fine-grained labels and the complexity of user-item
interactions. To alleviate these problems, we propose a Semi-Disentangled
Representation Learning method (SDRL) based on autoencoders. SDRL divides each
user/item embedding into two parts: the explainable and the unexplainable, so
as to improve proper disentanglement while preserving complex information in
representation. The explainable part consists of $internal\ block$ for
individual-based features and $external\ block$ for interaction-based features.
The unexplainable part is composed by $other\ block$ for other remaining
information. Experimental results on three real-world datasets demonstrate that
the proposed SDRL could not only effectively express user and item features but
also improve the explainability and generality compared with existing
representation methods.
