Outlier detection with explanations on music streaming data: A case study with danmark music group ltd.

Jonas Herskind Sejr*, Thorbjørn Christiansen, Nicolai Dvinge, Dan Hougesen, Peter Schneider-Kamp, Arthur Zimek

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

In the digital marketplaces, businesses can micro-monitor sales worldwide and in real-time. Due to the vast amounts of data, there is a pressing need for tools that automatically highlight changing trends and anomalous (outlier) behavior that is potentially interesting to users. In collaboration with Danmark Music Group Ltd. we developed an unsupervised system for this problem based on a predictive neural network. To make the method transparent to developers and users (musicians, music managers, etc.), the system delivers two levels of outlier explanations: the deviation from the model prediction, and the explanation of the model prediction. We demonstrate both types of outlier explanations to provide value to data scientists and developers during development, tuning, and evaluation. The quantitative and qualitative evaluation shows that the users find the identified trends and anomalies interesting and worth further investigation. Consequently, the system was integrated into the production system. We discuss the challenges in unsupervised parameter tuning and show that the system could be further improved with personalization and integration of additional information, unrelated to the raw outlier score.

Original languageEnglish
Article number2270
JournalApplied Sciences (Switzerland)
Volume11
Issue number5
Number of pages14
ISSN2076-3417
DOIs
Publication statusPublished - 1. Mar 2021

Bibliographical note

Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Keywords

  • Forecasting
  • Lstm
  • Outlier explanation
  • Unsupervised

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