  \textit{Knowledge-aware} recommendation methods (KGR) based on \textit{graph
neural networks} (GNNs) and \textit{contrastive learning} (CL) have achieved
promising performance. However, they fall short in modeling fine-grained user
preferences and further fail to leverage the \textit{preference-attribute
connection} to make predictions, leading to sub-optimal performance. To address
the issue, we propose a method named \textit{\textbf{K}nowledge-aware
\textbf{D}ual-side \textbf{A}ttribute-enhanced \textbf{R}ecommendation} (KDAR).
Specifically, we build \textit{user preference representations} and
\textit{attribute fusion representations} upon the attribute information in
knowledge graphs, which are utilized to enhance \textit{collaborative
filtering} (CF) based user and item representations, respectively. To
discriminate the contribution of each attribute in these two types of
attribute-based representations, a \textit{multi-level collaborative alignment
contrasting} mechanism is proposed to align the importance of attributes with
CF signals. Experimental results on four benchmark datasets demonstrate the
superiority of KDAR over several state-of-the-art baselines. Further analyses
verify the effectiveness of our method. The code of KDAR is released at:
\href{https://github.com/TJTP/KDAR}{https://github.com/TJTP/KDAR}.
