Multi-domain recommender systems benefit from cross-domain representation
learning and positive knowledge transfer. Both can be achieved by introducing a
specific modeling of input data (i.e. disjoint history) or trying dedicated
training regimes. At the same time, treating domains as separate input sources
becomes a limitation as it does not capture the interplay that naturally exists
between domains. In this work, we efficiently learn multi-domain representation
of sequential users' interactions using graph neural networks. We use temporal
intra- and inter-domain interactions as contextual information for our method
called MAGRec (short for Multi-domAin Graph-based Recommender). To better
capture all relations in a multi-domain setting, we learn two graph-based
sequential representations simultaneously: domain-guided for recent user
interest, and general for long-term interest. This approach helps to mitigate
the negative knowledge transfer problem from multiple domains and improve
overall representation. We perform experiments on publicly available datasets
in different scenarios where MAGRec consistently outperforms state-of-the-art
methods. Furthermore, we provide an ablation study and discuss further
extensions of our method.

本文提出了一种名为 MAGRec 的方法，通过图神经网络在多域序列用户交互中进行高效学习，利用时间内和跨领域交互作为上下文信息进行学习，以解决多个域的负面知识转移问题并改善整体表示效果。实验表明，在不同场景下 MAGRec 一直优于现有的最先进方法。

利用图结构化跨域表示进行多领域推荐

Exploiting Graph Structured Cross-Domain Representation for Multi-Domain Recommendation

Recently multi-domain recommender systems have received much attention from
researchers because they can solve cold-start problem as well as support for
cross-selling. However, when applying into multi-domain items, although
algorithms specifically addressing a single domain have many difficulties in
capturing the specific characteristics of each domain, multi-domain algorithms
have less opportunity to obtain similar features among domains. Because both
similarities and differences exist among domains, multi-domain models must
capture both to achieve good performance. Other studies of multi-domain systems
merely transfer knowledge from the source domain to the target domain, so the
source domain usually comes from external factors such as the search query or
social network, which is sometimes impossible to obtain. To handle the two
problems, we propose a model that can extract both homogeneous and divergent
features among domains and extract data in a domain can support for other
domain equally: a so-called Domain-to-Domain Translation Model (D2D-TM). It is
based on generative adversarial networks (GANs), Variational Autoencoders
(VAEs), and Cycle-Consistency (CC) for weight-sharing. We use the user
interaction history of each domain as input and extract latent features through
a VAE-GAN-CC network. Experiments underscore the effectiveness of the proposed
system over state-of-the-art methods by a large margin.

提出了一种基于生成对抗网络（GANs）、变分自动编码器（VAEs）和 Cycle-Consistency（CC）相结合的 D2D-TM 模型，用于解决多领域推荐系统在提取同构和散发特征方面遇到的问题，并证明其在性能优化方面超过了现有技术。