  Recommender systems (RS) commonly retrieve potential candidate items for
users from a massive number of items by modeling user interests based on
historical interactions. However, historical interaction data is highly sparse,
and most items are long-tail items, which limits the representation learning
for item discovery. This problem is further augmented by the discovery of novel
or cold-start items. For example, after a user displays interest in bitcoin
financial investment shows in the podcast space, a recommender system may want
to suggest, e.g., a newly released blockchain episode from a more technical
show. Episode correlations help the discovery, especially when interaction data
of episodes is limited. Accordingly, we build upon the classical Two-Tower
model and introduce the novel Multi-Source Augmentations using a Contrastive
Learning framework (MSACL) to enhance episode embedding learning by
incorporating positive episodes from numerous correlated semantics. Extensive
experiments on a real-world podcast recommendation dataset from a large audio
streaming platform demonstrate the effectiveness of the proposed framework for
user podcast exploration and cold-start episode recommendation.
