  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.
