Efficient Distributed MST Based Clustering for Recommender System

Coenen, Frans ORCID: 0000-0003-1026-6649 and Shahzad, Ahmad (2021) Efficient Distributed MST Based Clustering for Recommender System. In: IEEE International Conference on Data Minimg (ICDM) Workshop on Advanced Neural Algorithms and Theories for Recommender Systems (NeuRec), 2020-11-17 - 2020-11-20.

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Official URL: http://dx.doi.org/10.1109/icdmw51313.2020.00037

Abstract

This paper presents the Distributed Kruskal Algorithm for Minimum Spanning Tree (MST) based clustering to be used in the context of recommendation engines. The algorithm can operate over large graph data sets distributed over a number of machines. The operation of the algorithm is evaluated by comparing both the quality of the cluster configurations produced, and the accuracy of the predictions, with non-MST based clustering approaches. The results indicate that the proposed approach produces comparable recommendations at much lower storage, hence runtime, costs.

Item Type:	Conference or Workshop Item (Unspecified)
Uncontrolled Keywords:	Minimum Spanning Tree based Clustering, Recommendation Engines
Divisions:	Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User:	Symplectic Admin
Date Deposited:	21 Jun 2022 09:03
Last Modified:	18 Jan 2023 20:57
DOI:	10.1109/ICDMW51313.2020.00037
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3156878