The sequential interaction network usually find itself in a variety of applications, e.g., recommender system. Herein, inferring future interaction is of fundamental importance, and previous efforts are mainly focused on the dynamics in the classic zero-curvature Euclidean space. Despite the promising results achieved by previous methods, a range of significant issues still largely remains open: On the bipartite nature, is it appropriate to place user and item nodes in one identical space regardless of their inherent difference? On the network dynamics, instead of a fixed curvature space, will the representation spaces evolve when new interactions arrive continuously? On the learning paradigm, can we get rid of the label information costly to acquire? To address the aforementioned issues, we propose a novel Contrastive model for Sequential Interaction Network learning on Co-Evolving RiEmannian spaces, CSINCERE. To the best of our knowledge, we are the first to introduce a couple of co-evolving representation spaces, rather than a single or static space, and propose a co-contrastive learning for the sequential interaction network. In CSINCERE, we formulate a Cross-Space Aggregation for message-passing across representation spaces of different Riemannian geometries, and design a Neural Curvature Estimator based on Ricci curvatures for modeling the space evolvement over time. Thereafter, we present a Reweighed Co-Contrast between the temporal views of the sequential network, so that the couple of Riemannian spaces interact with each other for the interaction prediction without labels. Empirical results on 5 public datasets show the superiority of CSINCERE over the state-of-the-art methods.

翻译：序列交互网络常见于推荐系统等多种应用中。其中，推断未来交互具有根本重要性，先前的研究主要聚焦于经典零曲率欧几里得空间中的动力学。尽管现有方法取得了令人鼓舞的结果，但一系列重要问题仍亟待解决：在二部图性质方面，是否适合将用户和物品节点置于同一空间而忽略其固有差异？在网络动力学方面，表示空间是否会在新交互持续到来时演化，而非固定曲率空间？在学习范式方面，我们能否摆脱成本高昂的标签信息？针对上述问题，我们提出了一种新颖的对比模型，用于在共演化黎曼空间上进行序列交互网络学习，名为CSINCERE。据我们所知，我们是首个引入一对共演化表示空间（而非单一或静态空间）的工作，并提出了一种用于序列交互网络的共对比学习。在CSINCERE中，我们设计了跨空间聚合机制，用于在不同黎曼几何的表示空间之间传递消息，并基于里奇曲率设计了神经曲率估计器，以建模空间随时间演化的过程。随后，我们提出了序列网络时间视图之间的加权共对比，使这对黎曼空间相互交互，从而无需标签即可进行交互预测。在5个公开数据集上的实证结果表明，CSINCERE优于现有最先进方法。