# Learning Cross-Domain Representation with Multi-Graph Neural Network

arXiv: https://arxiv.org/abs/1905.10095 · HTML: https://mlsanity.com/paper/1905.10095/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 May 2019.

## Built on by (most cited first, 5 of 5)

- [Graph Learning under Distribution Shifts: A Comprehensive Survey on Domain Adaptation, Out-of-distribution, and Continual Learning](https://mlsanity.com/paper/2402.16374/) (2024)
- [The Evolution of Distributed Systems for Graph Neural Networks and their Origin in Graph Processing and Deep Learning: A Survey](https://mlsanity.com/paper/2305.13854/) (2023)
- [Multi-Graph based Multi-Scenario Recommendation in Large-scale Online Video Services](https://mlsanity.com/paper/2205.02446/) (2022)
- [Reducing Training Time in Cross-Silo Federated Learning using Multigraph Topology](https://mlsanity.com/paper/2207.09657/) (2022)
- [Mobility-Induced Graph Learning for WiFi Positioning](https://mlsanity.com/paper/2311.08271/) (2023)

## Abstract (arXiv)

Learning effective embedding has been proved to be useful in many real-world problems, such as recommender systems, search ranking and online advertisement. However, one of the challenges is data sparsity in learning large-scale item embedding, as users' historical behavior data are usually lacking or insufficient in an individual domain. In fact, user's behaviors from different domains regarding the same items are usually relevant. Therefore, we can learn complete user behaviors to alleviate the sparsity using complementary information from correlated domains. It is intuitive to model users' behaviors using graph, and graph neural networks (GNNs) have recently shown the great power for representation learning, which can be used to learn item embedding. However, it is challenging to transfer the information across domains and learn cross-domain representation using the existing GNNs. To address these challenges, in this paper, we propose a novel model - Deep Multi-Graph Embedding (DMGE) to learn cross-domain representation. Specifically, we first construct a multi-graph based on users' behaviors from different domains, and then propose a multi-graph neural network to learn cross-domain representation in an unsupervised manner. Particularly, we present a multiple-gradient descent optimizer for efficiently training the model. We evaluate our approach on various large-scale real-world datasets, and the experimental results show that DMGE outperforms other state-of-art embedding methods in various tasks.