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 Title: Towards a better understanding of music playlist titles and descriptions Author(s): Hao, Yun Director of Research: Downie, J. Stephen Doctoral Committee Chair(s): Downie, J. Stephen Doctoral Committee Member(s): Hu, Xiao; Torvik, Vetle; Ehmann, Andreas; Bosch, Nigel Department / Program: Information Sciences Discipline: Information Sciences Degree Granting Institution: University of Illinois at Urbana-Champaign Degree: Ph.D. Genre: Dissertation Subject(s): natural language processing recommender systems Abstract: Music playlists, either user-generated or curated by music streaming services, often come with titles and descriptions. Although informative, these titles and descriptions make up a sparse and noisy semantic space that is challenging to be leveraged for tasks such as making music recommendations. This dissertation is dedicated to developing a better understanding of playlist titles and descriptions by leveraging track sequences in playlists. Specifically, work has been done to capture latent patterns in tracks by an embedding approach, and the latent patterns are found to be well aligned with the organizing principles of mixtapes identified more than a decade ago. The effectiveness of the latent patterns is evaluated by the task of generating descriptive keywords/tags for playlists given tracks, indicating that the latent patterns learned from tracks in playlists are able to provide a good understanding of playlist titles and descriptions. The identified latent patterns are further leveraged to improve model performance on the task of predicting missing tracks given playlist titles and descriptions. Experimental results show that the proposed models yield improvements to the task, especially when playlist descriptions are provided as model input in addition to titles. The main contributions of this work include (1) providing a better solution to dealing with cold-start'' playlists in music recommender systems, and (2) proposing an effective approach to automatically generating descriptive keywords/tags for playlists using track sequences. Issue Date: 2021-04-20 Type: Thesis URI: http://hdl.handle.net/2142/110509 Rights Information: Copyright 2021 Yun Hao Date Available in IDEALS: 2021-09-17 Date Deposited: 2021-05
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