Knowledge-aware Dual-side Attribute-enhanced Recommendation
<|reference_start|>Knowledge-aware Dual-side Attribute-enhanced Recommendation: \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}.<|reference_end|>
@article{pang2024knowledge-aware,
    title={Knowledge-aware Dual-side Attribute-enhanced Recommendation},
    author={Taotian Pang, Xingyu Lou, Fei Zhao, Zhen Wu, Kuiyao Dong, Qiuying
  Peng, Yue Qi, Xinyu Dai},
    journal={arXiv preprint arXiv:2403.16037},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.16037},
    primaryClass={cs.IR}
}