Session-based recommendation (SBR) aims to predict the following item a user will interact with during an ongoing session. Most existing SBR models focus on designing sophisticated neural-based encoders to learn a session representation, capturing the relationship among session items. However, they tend to focus on the last item, neglecting diverse user intents that may exist within a session. This limitation leads to significant performance drops, especially for longer sessions. To address this issue, we propose a novel SBR model, called Multi-intent-aware Session-based Recommendation Model (MiaSRec). It adopts frequency embedding vectors indicating the item frequency in session to enhance the information about repeated items. MiaSRec represents various user intents by deriving multiple session representations centered on each item and dynamically selecting the important ones. Extensive experimental results show that MiaSRec outperforms existing state-of-the-art SBR models on six datasets, particularly those with longer average session length, achieving up to 6.27% and 24.56% gains for MRR@20 and Recall@20. Our code is available at https://github.com/jin530/MiaSRec.

翻译：会话推荐（Session-based Recommendation, SBR）旨在预测用户在正在进行会话中接下来会交互的项目。现有的大多数SBR模型聚焦于设计复杂的神经编码器以学习会话表示，捕捉会话项目之间的关系。然而，它们往往只关注最后一个项目，忽略了会话中可能存在的多样化用户意图。这一局限导致性能显著下降，尤其是在较长的会话中。为解决此问题，我们提出一种新型SBR模型，称为多意图感知会话推荐模型（MiaSRec）。该模型采用频率嵌入向量表示项目在会话中的出现频率，以增强重复项目的信息。MiaSRec通过基于每个会话项目导出多个会话表示并动态选择重要表示，来表征用户的多种意图。大量实验结果表明，MiaSRec在六个数据集上均优于现有最先进的SBR模型，尤其在平均会话长度较长的数据集上，MRR@20和Recall@20分别提升高达6.27%和24.56%。我们的代码开源在 https://github.com/jin530/MiaSRec。