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

arXiv: https://arxiv.org/abs/2110.13340 · HTML: https://mlsanity.com/paper/2110.13340/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 42 references that are in the map.
- First posted on arXiv 26 Oct 2021.

## Builds on (most cited first, 14 of 14)

- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data](https://mlsanity.com/paper/2003.06505/) (2020)
- [Variational Autoencoders for Collaborative Filtering](https://mlsanity.com/paper/1802.05814/) (2018)
- [Deep Learning based Recommender System: A Survey and New Perspectives](https://mlsanity.com/paper/1707.07435/) (2017)
- [Neural Collaborative Filtering vs. Matrix Factorization Revisited](https://mlsanity.com/paper/2005.09683/) (2020)
- [Cross-Domain Recommendation: Challenges, Progress, and Prospects](https://mlsanity.com/paper/2103.01696/) (2021)
- [Graph Learning based Recommender Systems: A Review](https://mlsanity.com/paper/2105.06339/) (2021)
- [A Deep Framework for Cross-Domain and Cross-System Recommendations](https://mlsanity.com/paper/2009.06215/) (2020)
- [Research Commentary on Recommendations with Side Information: A Survey and Research Directions](https://mlsanity.com/paper/1909.12807/) (2019)
- [A Unified Framework for Cross-Domain and Cross-System Recommendations](https://mlsanity.com/paper/2108.07976/) (2021)
- [Transfer Learning via Contextual Invariants for One-to-Many Cross-Domain Recommendation](https://mlsanity.com/paper/2005.10473/) (2020)
- [Information Laundering for Model Privacy](https://mlsanity.com/paper/2009.06112/) (2020)
- [Training Deep AutoEncoders for Collaborative Filtering](https://mlsanity.com/paper/1708.01715/) (2017)
- [A Distributed Collaborative Filtering Algorithm Using Multiple Data Sources](https://mlsanity.com/paper/1807.05853/) (2018)

## Abstract (arXiv)

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.