Learning vectorial representations of users and items from their interaction data is a core approach for Collaborative Filtering. While a user’s or an item’s representation is commonly built upon low-hop features such as IDs and interaction history, some recent works argue the existence of higher-hop interactions, thereby motivating the use of multi-hop topological knowledge in representation learning. However, existing methods in this area explore only the local pathological connections and thus ignore the overall semantics along paths. To this end, this paper introduces a new CF approach that learns to explicitly inject the multi-hop topological features of a user or an item as a whole into its representation in an end-to-end manner. Specifically, we explore the multi-hop topology via the paths connecting a user or an item to its neighbors at different hops. To capture the entire topological information, we seamlessly integrate aggregator function with a recurrent neural network to jointly extract salient neighborhood information and detect the pathological semantics. We develop two neural network models, DF-CTF and DW-CTF, where the former focuses on modeling each individual path and the latter focuses on adapting to the path entanglement in multi-hop structures. Furthermore, we evaluate our proposed approach on three real-world benchmark datasets and demonstrate its superior performance against state-of-the-art methods.

从用户和项的交互数据中学习用户和项的矢量表示是协作过滤的一种核心方法。虽然用户或物品的表示形式通常是基于低跳功能（例如ID和交互历史记录）构建的，但一些近期的著作却提出了更高跳的交互作用的存在，从而激励了在表示学习中使用多跳拓扑知识。然而，该领域中的现有方法仅探索局部病理联系，因此忽略了沿路径的整体语义。为此，本文介绍了一种新的CF方法，该方法学习将用户或整个项目的多跳拓扑特征显式地注入到端到端的表示中方式。具体而言，我们通过将用户或商品连接到不同跳数的邻居的路径来探索多跳拓扑。为了捕获整个拓扑信息，我们将聚合器功能与递归神经网络无缝集成，以共同提取显着的邻域信息并检测病理语义。我们开发了两个神经网络模型DF-CTF和DW-CTF，其中前者专注于为每个单独的路径建模，而后者则专注于适应多跳结构中的路径纠缠。此外，我们在三个现实世界的基准数据集上评估了我们提出的方法，并展示了其相对于最新方法的优越性能。