University of Illinois Urbana-Champaign

Automated Playlist Continuation with Apache PredictionIO

Hahn, James F.

Content Files
The Code4Lib Journal – Automated Playlist Continuation with Apache PredictionIO.pdf

Permalink

https://hdl.handle.net/2142/101915

Description

Title
Automated Playlist Continuation with Apache PredictionIO
Author(s)
Hahn, James F.
Contributor(s)
  • Ryckman, Benjamin
  • Ryckman, Nate
Issue Date
2018-11-08
Keyword(s)
  • personalization
  • recommender system
  • reconciliation
  • VIAF
  • music recommender systems
Date of Ingest
2018-11-08T16:18:40Z
Abstract
The Minrva project team, a software development research group based at the University of Illinois Library, developed a data-focused recommender system to participate in the creative track of the 2018 ACM RecSys Challenge, which focused on music recommendation. We describe here the large-scale data processing the Minrva team researched and developed for foundational reconciliation of the Million Playlist Dataset using external authority data on the web (e.g. VIAF, WikiData). The secondary focus of the research was evaluating and adapting the processing tools that support data reconciliation. This paper reports on the playlist enrichment process, indexing, and subsequent recommendation model developed for the music recommendation challenge.
Publisher
Code4Lib
Type of Resource
  • text
  • image
Genre of Resource
Article
Language
en
Permalink
  • https://journal.code4lib.org/articles/13850
  • http://hdl.handle.net/2142/101915
Copyright and License Information
This work is licensed under a Creative Commons Attribution 3.0 United States License.

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