论文标题
基于会话的建议
Graph Spring Network and Informative Anchor Selection for Session-based Recommendation
论文作者
论文摘要
基于会话的建议（SBR）旨在预测正在进行的匿名会话的下一个项目。 SBR的主要挑战是如何捕获项目之间的更丰富的关系并学习基于ID的项目嵌入以捕获这种关系。最近的研究建议首先从会话中构造项目图，并采用图形神经网络（GNN）来编码从图形中嵌入的项目。尽管这种基于图的方法已实现了性能的提高，但它们的GNN不适合基于ID的SBR任务嵌入学习。在本文中，我们认为这种基于ID的嵌入学习的目的是捕获一种\ textIt {邻域亲和力}，因为嵌入节点与嵌入空间中的邻居的嵌入类似。我们提出了一个名为Graph Spring Network（GSN）的新图形神经网络，用于学习基于ID的项目嵌入项目图，以优化嵌入空间中的邻域亲和力。此外，我们认为，即使堆叠多个GNN层也可能不足以编码图中两个项目节点的潜在关系。在本文中，我们提出了一种策略，该策略首先选择了一些有益的项目锚，然后对项目的潜在关系编码与此类锚的潜在关系。总而言之，我们为SBR任务提供了一个GSN-ias模型（图弹簧网络和信息性的锚定选择）。我们首先构建一个项目图来描述所有会话中项目的共发生。我们设计用于基于ID的项目嵌入学习的GSN，并提出\ textIt {item entropy}测量以选择信息性锚点。然后，我们设计了一种无监督的学习机制，以编码项目与锚的关系。接下来，我们采用共享的封闭式复发单元（GRU）网络来学习两个会话表示，并做出两个下一个项目预测。最后，我们设计了一种自适应决策融合策略，以融合两个预测以做出最终建议。
Session-based recommendation (SBR) aims at predicting the next item for an ongoing anonymous session. The major challenge of SBR is how to capture richer relations in between items and learn ID-based item embeddings to capture such relations. Recent studies propose to first construct an item graph from sessions and employ a Graph Neural Network (GNN) to encode item embedding from the graph. Although such graph-based approaches have achieved performance improvements, their GNNs are not suitable for ID-based embedding learning for the SBR task. In this paper, we argue that the objective of such ID-based embedding learning is to capture a kind of \textit{neighborhood affinity} in that the embedding of a node is similar to that of its neighbors' in the embedding space. We propose a new graph neural network, called Graph Spring Network (GSN), for learning ID-based item embedding on an item graph to optimize neighborhood affinity in the embedding space. Furthermore, we argue that even stacking multiple GNN layers may not be enough to encode potential relations for two item nodes far-apart in a graph. In this paper, we propose a strategy that first selects some informative item anchors and then encode items' potential relations to such anchors. In summary, we propose a GSN-IAS model (Graph Spring Network and Informative Anchor Selection) for the SBR task. We first construct an item graph to describe items' co-occurrences in all sessions. We design the GSN for ID-based item embedding learning and propose an \textit{item entropy} measure to select informative anchors. We then design an unsupervised learning mechanism to encode items' relations to anchors. We next employ a shared gated recurrent unit (GRU) network to learn two session representations and make two next item predictions. Finally, we design an adaptive decision fusion strategy to fuse two predictions to make the final recommendation.
