Name
Abstract | ||
|---|---|---|
ABSTRACTGraph convolutional networks (GCNs) have recently enabled a popular class of algorithms for collaborative filtering (CF). Nevertheless, the theoretical underpinnings of their empirical successes remain elusive. In this paper, we endeavor to obtain a better understanding of GCN-based CF methods via the lens of graph signal processing. By identifying the critical role of smoothness, a key concept in graph signal processing, we develop a unified graph convolution-based framework for CF. We prove that many existing CF methods are special cases of this framework, including the neighborhood-based methods, low-rank matrix factorization, linear auto-encoders, and LightGCN, corresponding to different low-pass filters. Based on our framework, we then present a simple and computationally efficient CF baseline, which we shall refer to as Graph Filter based Collaborative Filtering (GF-CF). Given an implicit feedback matrix, GF-CF can be obtained in a closed form instead of expensive training with back-propagation. Experiments will show that GF-CF achieves competitive or better performance against deep learning-based methods on three well-known datasets, notably with a 70% performance gain over LightGCN on the Amazon-book dataset. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1145/3459637.3482264 | Conference on Information and Knowledge Management |
DocType | Citations | PageRank |
Conference | 5 | 0.40 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yifei Shen | 1 | 5 | 1.07 |
| Yongji Wu | 2 | 5 | 0.40 |
| Yao Zhang | 3 | 5 | 1.41 |
| Caihua Shan | 4 | 5 | 2.42 |
| Jun Zhang | 5 | 3772 | 190.36 |
| K. B. Letaief | 6 | 11078 | 879.10 |
| Dongsheng Li | 7 | 5 | 0.40 |