  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.
