CDRec: Cayley-Dickson Recommender
This work addresses recommendation systems for users, presenting a novel approach that combines hypercomplex algebras with graph convolution, though it appears incremental as it builds on existing techniques in a new context.
The paper tackles the problem of recommendation systems by introducing the Cayley-Dickson construction to define hypercomplex algebras and designing a graph convolution operator for learning representations in hypercomplex space, achieving superior performance compared to state-of-the-art methods on real-world datasets.
In this paper, we propose a recommendation framework named Cayley-Dickson Recommender. We introduce Cayley-Dickson construction which uses a recursive process to define hypercomplex algebras and their mathematical operations. We also design a graph convolution operator to learn representations in the hypercomplex space. To the best of our knowledge, it is the first time that Cayley-Dickson construction and graph convolution techniques have been used in hypercomplex recommendation. Compared with the state-of-the-art recommendation methods, our method achieves superior performance on real-world datasets.