Recommender systems aim to provide personalized services to users and are playing an increasingly important role in our daily lives. The key of recommender systems is to predict how likely users will interact with items based on their historical online behaviors, e.g., clicks, add-to-cart, purchases, etc. To exploit these user-item interactions, there are increasing efforts on considering the user-item interactions as a user-item bipartite graph and then performing information propagation in the graph via Graph Neural Networks (GNNs). Given the power of GNNs in graph representation learning, these GNN-based recommendation methods have remarkably boosted the recommendation performance. Despite their success, most existing GNN-based recommender systems overlook the existence of interactions caused by unreliable behaviors (e.g., random/bait clicks) and uniformly treat all the interactions, which can lead to sub-optimal and unstable performance. In this paper, we investigate the drawbacks (e.g., non-adaptive propagation and non-robustness) of existing GNN-based recommendation methods. To address these drawbacks, we propose the Graph Trend Networks for recommendations (GTN) with principled designs that can capture the adaptive reliability of the interactions. Comprehensive experiments and ablation studies are presented to verify and understand the effectiveness of the proposed framework. Our implementation and datasets can be released after publication.
翻译:建议系统的关键是预测用户根据以往在线行为,例如点击、添加到目录、购买等,与项目进行互动的可能性。为了利用这些用户项目互动,人们正在加紧努力,将用户项目互动视为用户项目双向双向图,然后通过图示神经网络(GNNs)在图表中进行信息传播。鉴于GNNs在图形演示学习中的力量,这些基于GNN的推荐方法大大增强了建议性能。尽管取得了成功,但大多数基于GNN的推荐系统忽略了不可靠行为(例如随机/测试点击)造成的互动的存在,并一致对待所有互动,这可能导致亚优和不稳定的性能。在本文中,我们研究了基于GNN的建议方法的缺点(例如,非适应性传播和非破坏性传播),这些方法大大增强了建议性。为了解决这些不可靠行为(例如随机/测试)造成的互动问题,我们提议对GNNN的建议进行可靠性测试,我们提出的数据库数据库的模型可以用来进行升级和升级。我们提议的GNNNN的建议可以用来对数据库的模型进行精确性分析。我们提出的数据分析后,我们提出的数据库的模型的模型的模型和数据库的模型的模型的模型的模型可以用来进行核查。