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Title:Automated Playlist Continuation with Apache PredictionIO
Author(s):Hahn, James F.
Contributor(s):Ryckman, Benjamin; Ryckman, Nate
Subject(s):personalization
recommender system
reconciliation
VIAF
music recommender systems
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.
Issue Date:2018-11-08
Publisher:Code4Lib
Citation Info:Hahn, J. (2018). Automated Playlist Continuation with Apache PredictionIO. Code4Lib Journal, 42. https://journal.code4lib.org/articles/13850
Genre:Article
Type:Text
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Language:English
URI:https://journal.code4lib.org/articles/13850
http://hdl.handle.net/2142/101915
Rights Information:This work is licensed under a Creative Commons Attribution 3.0 United States License.
Date Available in IDEALS:2018-11-08


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