# Spectral Collaborative Filtering

arXiv: https://arxiv.org/abs/1808.10523 · HTML: https://mlsanity.com/paper/1808.10523/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Aug 2018.

## Built on by (most cited first, 30 of 34)

- [Graph-ToolFormer: To Empower LLMs with Graph Reasoning Ability via Prompt Augmented by ChatGPT](https://mlsanity.com/paper/2304.11116/) (2023)
- [A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation](https://mlsanity.com/paper/2104.13030/) (2021)
- [How Powerful is Graph Convolution for Recommendation?](https://mlsanity.com/paper/2108.07567/) (2021)
- [Contrastive Meta Learning with Behavior Multiplicity for Recommendation](https://mlsanity.com/paper/2202.08523/) (2022)
- [Dynamic Graph Collaborative Filtering](https://mlsanity.com/paper/2101.02844/) (2021)
- [Embedding in Recommender Systems: A Survey](https://mlsanity.com/paper/2310.18608/) (2023)
- [DaisyRec 2.0: Benchmarking Recommendation for Rigorous Evaluation](https://mlsanity.com/paper/2206.10848/) (2022)
- [RGRecSys: A Toolkit for Robustness Evaluation of Recommender Systems](https://mlsanity.com/paper/2201.04399/) (2022)
- [SiReN: Sign-Aware Recommendation Using Graph Neural Networks](https://mlsanity.com/paper/2108.08735/) (2021)
- [Position-enhanced and Time-aware Graph Convolutional Network for Sequential Recommendations](https://mlsanity.com/paper/2107.05235/) (2021)
- [Turbo-CF: Matrix Decomposition-Free Graph Filtering for Fast Recommendation](https://mlsanity.com/paper/2404.14243/) (2024)
- [Graph Spectral Filtering with Chebyshev Interpolation for Recommendation](https://mlsanity.com/paper/2505.00552/) (2025)
- [Siamese Attribute-missing Graph Auto-encoder](https://mlsanity.com/paper/2112.04842/) (2021)
- [GEDI: A Graph-based End-to-end Data Imputation Framework](https://mlsanity.com/paper/2208.06573/) (2022)
- [Criteria Tell You More than Ratings: Criteria Preference-Aware Light Graph Convolution for Effective Multi-Criteria Recommendation](https://mlsanity.com/paper/2305.18885/) (2023)
- [Initialization Matters: Regularizing Manifold-informed Initialization for Neural Recommendation Systems](https://mlsanity.com/paper/2106.04993/) (2021)
- [On the Generalizability and Predictability of Recommender Systems](https://mlsanity.com/paper/2206.11886/) (2022)
- [Time-aware Hyperbolic Graph Attention Network for Session-based Recommendation](https://mlsanity.com/paper/2301.03780/) (2023)
- [Challenging the Myth of Graph Collaborative Filtering: a Reasoned and Reproducibility-driven Analysis](https://mlsanity.com/paper/2308.00404/) (2023)
- [FedHCDR: Federated Cross-Domain Recommendation with Hypergraph Signal Decoupling](https://mlsanity.com/paper/2403.02630/) (2024)
- [Criteria-Aware Graph Filtering: Extremely Fast Yet Accurate Multi-Criteria Recommendation](https://mlsanity.com/paper/2502.09046/) (2025)
- [Hgformer: Hyperbolic Graph Transformer for Recommendation](https://mlsanity.com/paper/2502.15693/) (2024)
- [A Scalable Recommendation Engine for New Users and Items](https://mlsanity.com/paper/2209.06128/) (2022)
- [Memory Is No Longer a Bottleneck: Memory-Efficient Graph Filtering for Scalable Collaborative Filtering](https://mlsanity.com/paper/2606.21540/) (2026)
- [An Algorithm for Recommending Groceries Based on an Item Ranking Method](https://mlsanity.com/paper/2105.00650/) (2021)
- [KQGC: Knowledge Graph Embedding with Smoothing Effects of Graph Convolutions for Recommendation](https://mlsanity.com/paper/2205.12102/) (2022)
- [SUPER-Rec: SUrrounding Position-Enhanced Representation for Recommendation](https://mlsanity.com/paper/2209.04154/) (2022)
- [Adaptive spectral graph wavelets for collaborative filtering](https://mlsanity.com/paper/2312.03167/) (2023)
- [A Recommender System for NFT Collectibles with Item Feature](https://mlsanity.com/paper/2403.18305/) (2024)
- [Measuring the Predictability of Recommender Systems using Structural Complexity Metrics](https://mlsanity.com/paper/2404.08829/) (2024)

## Abstract (arXiv)

Despite the popularity of Collaborative Filtering (CF), CF-based methods are haunted by the textitcold-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 textitspectral 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 textitspectral domain, SpectralCF is capable of discovering deep connections between users and items and therefore, alleviates the textitcold-start problem for CF. To the best of our knowledge, SpectralCF is the first CF-based method directly learning from the textitspectral 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 urlhttps://github.com/lzheng21/SpectralCF.