With the increasing development of e-commerce and online services, personalized recommendation systems have become crucial for enhancing user satisfaction and driving business revenue. Traditional sequential recommendation methods that rely on explicit item IDs encounter challenges in handling item cold start and domain transfer problems. Recent approaches have attempted to use modal features associated with items as a replacement for item IDs, enabling the transfer of learned knowledge across different datasets. However, these methods typically calculate the correlation between the model's output and item embeddings, which may suffer from inconsistencies between high-level feature vectors and low-level feature embeddings, thereby hindering further model learning. To address this issue, we propose a dual-tower retrieval architecture for sequence recommendation. In this architecture, the predicted embedding from the user encoder is used to retrieve the generated embedding from the item encoder, thereby alleviating the issue of inconsistent feature levels. Moreover, in order to further improve the retrieval performance of the model, we also propose a self-supervised multi-modal pretraining method inspired by the consistency property of contrastive learning. This pretraining method enables the model to align various feature combinations of items, thereby effectively generalizing to diverse datasets with different item features. We evaluate the proposed method on five publicly available datasets and conduct extensive experiments. The results demonstrate significant performance improvement of our method.

翻译：随着电子商务和在线服务的日益发展，个性化推荐系统在提升用户满意度和推动业务收入方面变得至关重要。传统的依赖显式物品ID的序列推荐方法在处理物品冷启动和领域迁移问题时面临挑战。近期研究尝试使用与物品关联的模态特征替代物品ID，从而在不同数据集间迁移所学知识。然而，这些方法通常计算模型输出与物品嵌入之间的相关性，可能导致高层特征向量与低层特征嵌入不一致，进而阻碍模型进一步学习。为解决此问题，我们提出一种用于序列推荐的双塔检索架构。在该架构中，用户编码器生成的预测嵌入用于检索物品编码器生成的嵌入，从而缓解特征层级不一致的问题。此外，为进一步提升模型检索性能，我们受对比学习一致性特性的启发，提出一种自监督多模态预训练方法。该预训练方法使模型能够对齐物品的各种特征组合，从而有效泛化至具有不同物品特征的多类数据集。我们在五个公开数据集上对所提方法进行了评估，并开展了大量实验。结果表明，我们的方法取得了显著的性能提升。