  Recommender Systems (RSs) are operated locally by different organizations in
many realistic scenarios. If various organizations can fully share their data
and perform computation in a centralized manner, they may significantly improve
the accuracy of recommendations. However, collaborations among multiple
organizations in enhancing the performance of recommendations are primarily
limited due to the difficulty of sharing data and models. To address this
challenge, we propose Decentralized Multi-Target Cross-Domain Recommendation
(DMTCDR) with Multi-Target Assisted Learning (MTAL) and Assisted AutoEncoder
(AAE). Our method can help multiple organizations collaboratively improve their
recommendation performance in a decentralized manner without sharing sensitive
assets. Consequently, it allows decentralized organizations to collaborate and
form a community of shared interest. We conduct extensive experiments to
demonstrate that the new method can significantly outperform locally trained
RSs and mitigate the cold start problem.
