Modeling sequential behaviors is the core of sequential recommendation. As users visit items in chronological order, existing methods typically capture a user’s present interests from his/her past-to-present behaviors, i.e., making recommendations with only the unidirectional past information. This article argues that future information is another critical factor for the sequential recommendation. However, directly learning from future-to-present behaviors inevitably causes data leakage. Here, it is pointed out that future information can be learned from users’ collaborative behaviors. Toward this end, this article introduces sequential graphs to depict item transition relationships: where and how each item transits from and will transit to. This temporal evolution information is called the light cone in special and general relativity. Then, a bidirectional sequential graph convolutional network (BiSGCN) is proposed to learn item representations by encoding past and future light cones. Finally, a manifold translating embedding (MTE) method is proposed to model item transition patterns in Riemannian manifolds, which helps to better capture the geometric structures of light cones and item transition patterns. Experimental comparisons and ablation studies verify the outstanding performance of BiSGCN, the benefits of learning from the future, and the improvements of learning in Riemannian manifolds.

中文翻译：

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向未来学习：用于顺序推荐的光锥建模

顺序行为建模是顺序推荐的核心。当用户按时间顺序访问项目时，现有方法通常从用户过去到现在的行为中捕获用户当前的兴趣，即仅利用单向的过去信息进行推荐。本文认为，未来信息是顺序推荐的另一个关键因素。然而，直接从未来到现在的行为中学习不可避免地会导致数据泄露。这里指出，可以从用户的协作行为中学习未来的信息。为此，本文引入了顺序图来描述项目转移关系：每个项目从何处以及如何转移以及将转移到何处。这种时间演化信息在狭义相对论和广义相对论中被称为光锥。然后，提出了一种双向顺序图卷积网络（BiSGCN），通过对过去和未来的光锥进行编码来学习项目表示。最后，提出了一种流形平移嵌入（MTE）方法来对黎曼流形中的项目转换模式进行建模，这有助于更好地捕获光锥的几何结构和项目转换模式。实验比较和消融研究验证了 BiSGCN 的出色性能、向未来学习的好处以及黎曼流形学习的改进。