Trajectory Based Podcast Recommendation
This addresses podcast recommendation for users, which is incremental as it adapts existing sequential methods to a new domain.
The paper tackled podcast recommendation by modeling users as moving sequentially through the podcast library and making recommendations based on their trajectory, resulting in a 450% increase in effectiveness over a collaborative filtering baseline.
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.