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application/pdf ![]() | Final published article |
Description
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 image |
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 |
This item appears in the following Collection(s)
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Faculty and Staff Research - University Library
Research and scholarship of the Library faculty and staff