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.
Cite
@article{arxiv.2112.08632,
title = {CDRec: Cayley-Dickson Recommender},
author = {Anchen Li and Bo Yang and Huan Huo and Farookh Hussain},
journal= {arXiv preprint arXiv:2112.08632},
year = {2022}
}
Comments
1. The Preliminary Section is not sufficient. 2. Figure 2 is not clear enough. 3. The Experiment Section are not sufficient