基于图生成过程的跨领域推荐

推荐系统在各方各面得到充分的应用，时刻影响着日常生活。要训练出一个良好的推荐系统往往需要大量的用户—商品交互数据，但是实际情况下获得的数据往往是十分稀疏的，这往往会使得训练出来的模型过拟合，最后难以获得理想的推荐效果。为了解决这个问题，跨领域推荐系统应运而生。目前大部分的跨领域推荐系统工作都是借鉴传统领域自适应的方法，使用基于特征对齐或者对抗学习的思想将领域不变用户兴趣从有丰富数据的源域迁移到稀疏的目标域上，例如豆瓣电影迁移到豆瓣图书。但是由于不同推荐平台的网络结构有所不同，现有方法暴力提取的领域不变的语义信息容易和结构信息耦合，导致错配现象。而且，现有方法忽略了图数据本身存在的噪声，导致实验效果进一步受到了影响。为了解决这个问题，首先引入了图数据的因果数据生成过程，通过领域特征隐变量和语义特征隐变量、噪声隐变量解耦出来，通过使用每个节点的语义隐变量进行推荐，从而获得领域不变的推荐效果。在多个公开数据集上验证了该方法，并取得了目前最好的实验效果。

Cross-domain recommendation under graph generation process

Recommendation systems are widely used everywhere and have a great influence on daily life. Aiming to train an ideal recommendation system, a massive of use-item interactive pairs should be provided. However, the dataset obtained is usually sparse, which might result in an over-fitting model and be hard to obtain the satisfying performance. In order to address this problem, the cross-domain recommendation is raised. Most of the existing methods on cross-domain recommendation systems borrow the ideas of the conventional unsupervised domain adaptation, which employ the feature alignment or adversarial training methods. Hence they can transfer the domain-invariant interests of users from the source to the target domains, e. g., from the Douban Movies to the Douban Books. However, since the network structures vary with different recommendation platforms, the existing methods on cross-domain recommendation systems straightforwardly extract the domain-invariant representation may entangle the structure information, which may result in the false alignment phenomenon. Furthermore, the previous efforts ignore the noise information behind the graph data, which further degenerate the experimental performance. In order to address the aforementioned problems, this paper brought the causal generation process of graph data into the cross-domain recommendation systems, this paper used the semantic latent variables of each node to calculate the relationships between users and items via disentangling the semantic latent variables, domain latent variables and noise latent variables. Experiments show that the proposed method yields state-of-the-art performance on several cross-domain recommendation system benchmark datasets.