Transfer Learning via Contextual Invariants for One-to-Many Cross-Domain Recommendation

Understand

The rapid proliferation of new users and items on the social web has aggravated the gray-sheep user/long-tail item challenge in recommender systems.

• Historically, cross-domain co-clustering methods have successfully leveraged shared users and items across dense and sparse domains to improve inference quality.
• However, they rely on shared rating data and cannot scale to multiple sparse target domains (i.e., the one-to-many transfer setting).
• This, combined with the increasing adoption of neural recommender architectures, motivates us to develop scalable neural layer-transfer approaches for cross-domain learning.