CDRec: Cayley-Dickson Recommender

TL;DR

This paper introduces a novel recommendation framework called Cayley-Dickson Recommender that leverages hypercomplex algebra and graph convolution techniques, achieving superior performance on real-world datasets.

Contribution

It is the first to combine Cayley-Dickson hypercomplex algebra with graph convolution for recommendation tasks, offering a new approach in the field.

Findings

Achieves better accuracy than existing methods on benchmark datasets.

Introduces a new hypercomplex representation learning technique.

Demonstrates the effectiveness of Cayley-Dickson construction in recommendation systems.

Abstract

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.