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Recommender Systems (RSs) are operated locally by different organizations inmany realistic scenarios. If various organizations can fully share their dataand perform computation in a centralized manner, they may significantly improvethe accuracy of recommendations. However, collaborations among multipleorganizations in enhancing the performance of recommendations are primarilylimited due to the difficulty of sharing data and models. To address thischallenge, 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 theirrecommendation performance in a decentralized manner without sharing sensitiveassets. Consequently, it allows decentralized organizations to collaborate andform a community of shared interest. We conduct extensive experiments todemonstrate that the new method can significantly outperform locally trainedRSs and mitigate the cold start problem.

Decentralized Multi-Target Cross-Domain Recommendation for Multi-Organization Collaborations