Session-based recommendation (SBR) is a task that aims to predict items based on anonymous sequences of user behaviors in a session. While there are methods that leverage rich context information in sessions for SBR, most of them have the following limitations: 1) they fail to distinguish the item-item edge types when constructing the global graph for exploiting cross-session contexts; 2) they learn a fixed embedding vector for each item, which lacks the flexibility to reflect the variation of user interests across sessions; 3) they generally use the one-hot encoded vector of the target item as the hard label to predict, thus failing to capture the true user preference. To solve these issues, we propose CARES, a novel context-aware session-based recommendation model with graph neural networks, which utilizes different types of contexts in sessions to capture user interests. Specifically, we first construct a multi-relation cross-session graph to connect items according to intra- and cross-session item-level contexts. Further, to encode the variation of user interests, we design personalized item representations. Finally, we employ a label collaboration strategy for generating soft user preference distribution as labels. Experiments on three benchmark datasets demonstrate that CARES consistently outperforms state-of-the-art models in terms of P@20 and MRR@20. Our data and codes are publicly available at https://github.com/brilliantZhang/CARES.

翻译：会话推荐（SBR）旨在根据匿名用户行为序列预测会话中的项目。现有方法虽能利用会话中的丰富上下文信息，但存在以下局限：1）在构建用于挖掘跨会话上下文的全局图时，无法区分项目-项目边类型；2）为每个项目学习固定嵌入向量，缺乏反映跨会话用户兴趣变化的灵活性；3）通常使用目标项目的独热编码向量作为硬标签进行预测，难以捕捉真实用户偏好。为解决上述问题，我们提出CARES——一种基于图神经网络的新型上下文感知会话推荐模型，该模型利用会话中的多类上下文信息捕捉用户兴趣。具体而言，我们首先构建多关系跨会话图，根据会话内与跨会话的项目级上下文建立项目关联；其次，为编码用户兴趣变化，设计个性化项目表征；最后，采用标签协同策略生成软标签形式的用户偏好分布。在三个基准数据集上的实验表明，CARES在P@20和MRR@20指标上持续优于现有最优模型。相关数据与代码已公开于https://github.com/brilliantZhang/CARES。