Similarity-inclusive Link Prediction with Quaternions

Zuhal Kurt, Ömer Gerek, Alper Bilge, Kemal Özkan

Abstract

This paper proposes a Quaternion-based link prediction method, a novel representation learning method for recommendation purposes. The proposed algorithm depends on and computation with Quaternion algebra, benefiting from the expressiveness and rich representation learning capability of the Hamilton products. The proposed method depends on a link prediction approach and reveals the significant potential for performance improvement in top-N recommendation tasks. The experimental results indicate the superior performance of the approach using two quality measurements – hits rate, and coverage - on the Movielens and Hetrec datasets. Additionally, extensive experiments are conducted on three subsets of the Amazon dataset to understand the flexibility of this algorithm to incorporate different information sources and demonstrate the effectiveness of Quaternion algebra in graph-based recommendation algorithms. The proposed algorithms obtain comparatively higher performance, they are improved with similarity factors. The results show that the proposed quaternion-based algorithm can effectively deal with the deficiencies in graph-based recommender system, making it a preferable alternative among the other available methods.

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Paper Citation


in Harvard Style

Kurt Z., Gerek Ö., Bilge A. and Özkan K. (2021). Similarity-inclusive Link Prediction with Quaternions. In Proceedings of the 23rd International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-758-509-8, pages 842-854. DOI: 10.5220/0010469808420854


in Bibtex Style

@conference{iceis21,
author={Zuhal Kurt and Ömer Gerek and Alper Bilge and Kemal Özkan},
title={Similarity-inclusive Link Prediction with Quaternions},
booktitle={Proceedings of the 23rd International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2021},
pages={842-854},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010469808420854},
isbn={978-989-758-509-8},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 23rd International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - Similarity-inclusive Link Prediction with Quaternions
SN - 978-989-758-509-8
AU - Kurt Z.
AU - Gerek Ö.
AU - Bilge A.
AU - Özkan K.
PY - 2021
SP - 842
EP - 854
DO - 10.5220/0010469808420854