Spectral Collaborative Filtering

TL;DR

SpectralCF introduces a novel spectral domain convolution for collaborative filtering, leveraging graph connectivity to better address the cold-start problem and outperform existing models.

Contribution

It is the first CF method to directly learn from spectral domains of user-item bipartite graphs, enhancing connection discovery.

Findings

SpectralCF significantly outperforms state-of-the-art models on standard datasets.

The spectral convolution captures both proximity and connectivity information.

The method effectively alleviates the cold-start problem in collaborative filtering.

Abstract

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 \url{https://github.com/lzheng21/SpectralCF}.