  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.
