Despite the popularity of Collaborative Filtering (CF), CF-based methods are
haunted by the \textit{cold-start} problem, which has a significantly negative
impact on users' experiences with Recommender Systems (RS). In this paper, to
overcome the aforementioned drawback, we first formulate the relationships
between users and items as a bipartite graph. Then, we propose a new spectral
convolution operation directly performing in the \textit{spectral domain},
where not only the proximity information of a graph but also the connectivity
information hidden in the graph are revealed. With the proposed spectral
convolution operation, we build a deep recommendation model called Spectral
Collaborative Filtering (SpectralCF). Benefiting from the rich information of
connectivity existing in the \textit{spectral domain}, SpectralCF is capable of
discovering deep connections between users and items and therefore, alleviates
the \textit{cold-start} problem for CF. To the best of our knowledge,
SpectralCF is the first CF-based method directly learning from the
\textit{spectral domains} of user-item bipartite graphs. We apply our method on
several standard datasets. It is shown that SpectralCF significantly
outperforms state-of-the-art models. Code and data are available at
https://github.com/lzheng21/SpectralCF.

本研究提出了一种利用谱域进行深度协同过滤的新模型 - Spectral Collaborative Filtering (SpectralCF)，能够在用户 - 物品二部图的谱域中直接进行学习，发挥图中的信息优势，解决协同过滤中的 “冷启动” 问题，并在多个标准数据集上明显优于现有模型。