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Authors: Okan Çiftçi ; Samet Tenekeci and Ceren Ülgentürk

Affiliation: Department of Computer Engineering, İzmir Institute of Technology, İzmir, Turkey

Keyword(s): Association Rule Mining, Community Detection, Recommender Systems, Graph Databases.

Abstract: Recent advances in the web have greatly increased the accessibility of music streaming platforms and the amount of consumable audio content. This has made automated recommendation systems a necessity for listeners and streaming platforms alike. Therefore, a wide variety of predictive models have been designed to identify related artists and music collections. In this paper, we proposed a graph-based approach that utilizes association rules extracted from Spotify playlists. We constructed several artist networks and identified related artist clusters using Louvain and Label Propagation community detection algorithms. We analyzed internal and external cluster agreements based on different validation criteria. As a result, we achieved up to 99.38% internal and 90.53% external agreements between our models and Spotify’s related artist lists. These results show that integrating association rule mining concepts with graph databases can be a novel and effective way to design an artist recom mendation system. (More)

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Paper citation in several formats:
Çiftçi, O.; Tenekeci, S. and Ülgentürk, C. (2021). Artist Recommendation based on Association Rule Mining and Community Detection. In Proceedings of the 13th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2021) - KDIR; ISBN 978-989-758-533-3; ISSN 2184-3228, SciTePress, pages 257-263. DOI: 10.5220/0010678600003064

@conference{kdir21,
author={Okan \c{C}ift\c{C}i. and Samet Tenekeci. and Ceren Ülgentürk.},
title={Artist Recommendation based on Association Rule Mining and Community Detection},
booktitle={Proceedings of the 13th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2021) - KDIR},
year={2021},
pages={257-263},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010678600003064},
isbn={978-989-758-533-3},
issn={2184-3228},
}

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2021) - KDIR
TI - Artist Recommendation based on Association Rule Mining and Community Detection
SN - 978-989-758-533-3
IS - 2184-3228
AU - Çiftçi, O.
AU - Tenekeci, S.
AU - Ülgentürk, C.
PY - 2021
SP - 257
EP - 263
DO - 10.5220/0010678600003064
PB - SciTePress

- Science and Technology Publications, Lda.
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