Name
Abstract | ||
|---|---|---|
ABSTRACT Hypercomplex algebras are well-developed in the area of mathematics. Recently, several hypercomplex recommendation approaches have been proposed and yielded great success. However, two vital issues have not been well-considered in existing hypercomplex recommenders. First, these methods are only designed for specific and low-dimensional hypercomplex algebras (e.g., complex and quaternion algebras), ignoring the exploration and utilization of high-dimensional ones. Second, most recommenders treat every user-item interaction as an isolated data instance, without considering high-order collaborative relationships. To bridge these gaps, in this paper, we propose a novel recommendation framework named HyperComplex Graph Collaborative Filtering (HCGCF). To study the high-dimensional hypercomplex algebras, we introduce Cayley–Dickson construction which utilizes a recursive process to define hypercomplex algebras and their mathematical operations. Based on Cayley–Dickson construction, we devise a hypercomplex graph convolution operator to learn user and item representations. Specifically, the operator models both the neighborhood summary and interaction relations with neighbors in hypercomplex spaces, effectively exploiting the high-order connectivity in the user-item bipartite graph. To the best of our knowledge, it is the first time that Cayley-Dickson construction and graph convolution techniques have been explicitly discussed and used in hypercomplex recommender systems. Compared with several state-of-the-art recommender baselines, HCGCF achieves superior performance in both click-through rate prediction and top-K recommendation on three real-world datasets. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1145/3485447.3512065 | International World Wide Web Conference |
Keywords | DocType | Citations |
Recommendation, Collaborative Filtering, Hypercomplex Spaces, Graph Convolutional Networks, Cayley-Dickson Construction | Conference | 0 |
PageRank | References | Authors |
0.34 | 5 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Anchen Li | 1 | 0 | 1.01 |
| Bo Yang | 2 | 0 | 0.34 |
| Huan Huo | 3 | 35 | 10.00 |
| Farookh Hussain | 4 | 0 | 0.34 |