论文标题
通过图神经网络为基于会话的建议建模复杂的依赖关系
Modeling Complex Dependencies for Session-based Recommendations via Graph Neural Networks
论文作者
论文摘要
基于会话的建议（SBRS）从会话中捕获项目的依赖项，以推荐下一个项目。近年来，基于图形神经网络（GNN）的SBR已成为SBR的主流，从GNN在建模复杂依赖性中的优越性中受益。基于对相邻依赖性的强烈假设，在大多数基于GNN的SBR中，会话中的任何两个相邻项目都必须取决于。但是，我们认为，由于用户行为的不确定性和复杂性，邻接不一定表明依赖性。但是，上述假设并不总是在实际的建议方案中存在，因此它可以很容易地导致两个缺点：（1）会话中发生错误的依赖性，因为存在相邻但不是真正的依赖项，并且（2）会话中的真实依赖性丢失在会话中发生，因为存在非附件，但实际上有依赖的项目。这些缺点显着影响项目表示学习，从而降低了下游建议性能。为了解决这些缺陷，我们提出了一种新颖的评论精制的项目间图神经网络（RI-GNN），该信息利用从项目评论中提取的主题信息来改善项目之间的依赖性。两个公共现实世界数据集的实验表明，RI-GNN的表现优于SOTA方法。
Session-based recommendations (SBRs) capture items' dependencies from the sessions to recommend the next item. In recent years, Graph neural networks (GNN) based SBRs have become the mainstream of SBRs benefited from the superiority of GNN in modeling complex dependencies. Based on a strong assumption of adjacent dependency, any two adjacent items in a session are necessarily dependent in most GNN-based SBRs. However, we argue that due to the uncertainty and complexity of user behaviors, adjacency does not necessarily indicate dependency. However, the above assumptions do not always hold in actual recommendation scenarios, so it can easily lead to two drawbacks: (1) false dependencies occur in the session because there are adjacent but not really dependent items, and (2) the missing of true dependencies occur in the session because there are non-adjacent but actually dependent items. These drawbacks significantly affect item representation learning, degrading the downstream recommendation performance. To address these deficiencies, we propose a novel review-refined inter-item graph neural network (RI-GNN), which utilizes topic information extracted from the reviews of items to improve dependencies between items. Experiments on two public real-world datasets demonstrate that RI-GNN outperforms SOTA methods.
