CDRec: Cayley-Dickson Recommender
Anchen Li, Bo Yang, Huan Huo, Farookh Hussain

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.
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Taxonomy
TopicsNeural Networks and Applications · Image Retrieval and Classification Techniques · Video Analysis and Summarization
MethodsConvolution
