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.
In this framework, all participants collaboratively optimize the aggregate of local loss functions, and each participant autonomously builds its own model by iteratively fitting the gradients of the overarching objective function.
We develop a general method called Parallel Assisted Learning that applies to the context where entities perform supervised learning and can collate their data according to a common data identifier. Under the PAL mechanism, a learning entity that receives assistance is obligated to assist others without the need to reveal any entity’s local data, model, and learning objective.
In our assisted learning framework, an organizational learner purchases assistance service from a service provider and aims to enhance its model performance within a few assistance rounds.
In this paper, we propose a method named ASCII for an agent to improve its classification performance through assistance from other agents.
In this work, we introduce the Assisted Learning framework for organizations to assist each other in supervised learning tasks without revealing any organization’s algorithm, data, or even task.