Self-Supervised Representation Learning with ID-Content Modality Alignment for Sequential Recommendation

Abstract

Sequential recommendation (SR) models often capture user preferences based on the historically interacted item IDs, which usually obtain sub-optimal performance when the interaction history is limited. Content-based sequential recommendation has recently emerged as a promising direction that exploits items' textual and visual features to enhance preference learning. However, there are still three key challenges: (i) how to reduce the semantic gap between different content modality representations; (ii) how to jointly model user behavior preferences and content preferences; and (iii) how to design an effective training strategy to align ID representations and content representations. To address these challenges, we propose a novel model, self-supervised representation learning with ID-Content modality alignment, named SICSRec. Firstly, we propose a LLM-driven sample construction method and develop a supervised fine-tuning approach to align item-level modality representations. Secondly, we design a novel Transformer-based sequential model, where an ID-modality sequence encoder captures user behavior preferences, a content-modality sequence encoder learns user content preferences, and a mix-modality sequence decoder grasps the intrinsic relationship between these two types of preferences. Thirdly, we propose a two-step training strategy with a content-aware contrastive learning task to align modality representations and ID representations, which decouples the training process of content modality dependency and item collaborative dependency. Extensive experiments conducted on four public video streaming datasets demonstrate our SICSRec outperforms the state-of-the-art ID-modality sequential recommenders and content-modality sequential recommenders by 8.04% on NDCG@5 and 6.62% on NDCD@10 on average, respectively.