Trajectory Based Podcast Recommendation

2020/09/08 by Greg Benton, Benton, Greg, Ghazal Fazelnia +5
Computer Science · Social Sciences · #FOS: Computer and information sciences #Human Mobility and Location-Based Analysis #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Music and Audio Processing #Recommender Systems and Techniques

paper · pdf · doi:10.48550/arxiv.2009.03859

openalex publication_date 2020/09/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Podcast recommendation is a growing area of research that presents new challenges and opportunities. Individuals interact with podcasts in a way that is distinct from most other media; and primary to our concerns is distinct from music consumption. We show that successful and consistent recommendations can be made by viewing users as moving through the podcast library sequentially. Recommendations for future podcasts are then made using the trajectory taken from their sequential behavior. Our experiments provide evidence that user behavior is confined to local trends, and that listening patterns tend to be found over short sequences of similar types of shows. Ultimately, our approach gives a450%increase in effectiveness over a collaborative filtering baseline.

Citations

• Embedding Entities and Relations for Learning and Inference in Knowledge Bases
• Long Short-Term Memory
• Sequence-Aware Recommender Systems
• PyTorch: An Imperative Style, High-Performance Deep Learning Library

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