Session-based recommendation techniques aim to capture dynamic user behavior by analyzing past interactions. However, existing methods heavily rely on historical item ID sequences to extract user preferences, leading to challenges such as popular bias and cold-start problems. In this paper, we propose a hybrid multimodal approach for session-based recommendation to address these challenges. Our approach combines different modalities, including textual content and item IDs, leveraging the complementary nature of these modalities using CatBoost. To learn universal item representations, we design a language representation-based item retrieval architecture that extracts features from the textual content utilizing pre-trained language models. Furthermore, we introduce a novel Decoupled Contrastive Learning method to enhance the effectiveness of the language representation. This technique decouples the sequence representation and item representation space, facilitating bidirectional alignment through dual-queue contrastive learning. Simultaneously, the momentum queue provides a large number of negative samples, effectively enhancing the effectiveness of contrastive learning. Our approach yielded competitive results, securing a 5th place ranking in KDD CUP 2023 Task 1. We have released the source code and pre-trained models associated with this work.

翻译：会话推荐技术旨在通过分析历史交互来捕捉动态用户行为。然而，现有方法严重依赖历史项目ID序列以提取用户偏好，导致流行度偏差和冷启动问题等挑战。本文提出一种混合多模态方法用于会话推荐以解决上述挑战。我们的方法结合了文本内容和项目ID等多种模态，利用CatBoost挖掘这些模态的互补特性。为学习通用项目表示，我们设计了一种基于语言表示的项目检索架构，利用预训练语言模型从文本内容中提取特征。此外，我们引入一种新颖的解耦对比学习方法以增强语言表示的有效性。该技术通过双队列对比学习实现序列表示与项目表示空间的解耦，促进双向对齐；同时，动量队列提供大量负样本，有效提升对比学习效果。我们的方法取得了具有竞争力的结果，在KDD CUP 2023任务一中获得第五名。我们已公开本工作相关的源代码与预训练模型。