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Recommending Podcasts for Cold-Start Users Based on Music Listening and Taste

Published: 25 July 2020 Publication History

Abstract

Recommender systems are increasingly used to predict and serve content that aligns with user taste, yet the task of matching new users with relevant content remains a challenge. We consider podcasting to be an emerging medium with rapid growth in adoption, and discuss challenges that arise when applying traditional recommendation approaches to address the cold-start problem. Using music consumption behavior, we examine two main techniques in inferring Spotify users preferences over more than 200k podcasts. Our results show significant improvements in consumption of up to 50% for both offline and online experiments. We provide extensive analysis on model performance and examine the degree to which music data as an input source introduces bias in recommendations.

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Cited By

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  • (2024)Structural Podcast Content Modeling with GeneralizabilityCompanion Proceedings of the ACM Web Conference 202410.1145/3589335.3651563(710-713)Online publication date: 13-May-2024
  • (2023)Enabling Goal-Focused Exploration of Podcasts in Interactive Recommender SystemsProceedings of the 28th International Conference on Intelligent User Interfaces10.1145/3581641.3584032(142-155)Online publication date: 27-Mar-2023
  • (2023)RESUS: Warm-up Cold Users via Meta-learning Residual User Preferences in CTR PredictionACM Transactions on Information Systems10.1145/356428341:3(1-26)Online publication date: 7-Feb-2023
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cover image ACM Conferences
SIGIR '20: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval
July 2020
2548 pages
ISBN:9781450380164
DOI:10.1145/3397271
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 25 July 2020

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Author Tags

  1. cold start recommendations
  2. cross-domain recommendations
  3. podcast recommendations

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Cited By

View all
  • (2024)Structural Podcast Content Modeling with GeneralizabilityCompanion Proceedings of the ACM Web Conference 202410.1145/3589335.3651563(710-713)Online publication date: 13-May-2024
  • (2023)Enabling Goal-Focused Exploration of Podcasts in Interactive Recommender SystemsProceedings of the 28th International Conference on Intelligent User Interfaces10.1145/3581641.3584032(142-155)Online publication date: 27-Mar-2023
  • (2023)RESUS: Warm-up Cold Users via Meta-learning Residual User Preferences in CTR PredictionACM Transactions on Information Systems10.1145/356428341:3(1-26)Online publication date: 7-Feb-2023
  • (2023)Video and Audio Linkage in Recommender SystemCooperative Design, Visualization, and Engineering10.1007/978-3-031-43815-8_18(181-192)Online publication date: 18-Sep-2023
  • (2022)Enhanced Graph Learning for Recommendation via Causal InferenceMathematics10.3390/math1011188110:11(1881)Online publication date: 31-May-2022
  • (2022)Practitioners Versus Users: A Value-Sensitive Evaluation of Current Industrial Recommender System DesignProceedings of the ACM on Human-Computer Interaction10.1145/35556466:CSCW2(1-32)Online publication date: 11-Nov-2022
  • (2022)Choice of Implicit Signal Matters: Accounting for User Aspirations in Podcast RecommendationsProceedings of the ACM Web Conference 202210.1145/3485447.3512115(2433-2441)Online publication date: 25-Apr-2022
  • (2022)Sequential Recommendation via Stochastic Self-AttentionProceedings of the ACM Web Conference 202210.1145/3485447.3512077(2036-2047)Online publication date: 25-Apr-2022
  • (2022)A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich RecommendationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.3145690(1-1)Online publication date: 2022
  • (2021)Current Challenges and Future Directions in Podcast Information AccessProceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3404835.3462805(1554-1565)Online publication date: 11-Jul-2021
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