Diffusion-based Contrastive Learning for Sequential Recommendation

• URL: http://arxiv.org/abs/2405.09369v4
• Date: Sun, 20 Oct 2024 11:05:15 GMT
• Title: Diffusion-based Contrastive Learning for Sequential Recommendation
• Authors: Ziqiang Cui, Haolun Wu, Bowei He, Ji Cheng, Chen Ma,
• Abstract summary: We propose a Context-aware Diffusion-based Contrastive Learning for Sequential Recommendation, named CaDiRec. CaDiRec employs a context-aware diffusion model to generate alternative items for the given positions within a sequence. We train the entire framework in an end-to-end manner, with shared item embeddings between the diffusion model and the recommendation model.
• Score: 6.3482831836623355
• License:
• Abstract: Contrastive learning has been effectively utilized to enhance the training of sequential recommendation models by leveraging informative self-supervised signals. Most existing approaches generate augmented views of the same user sequence through random augmentation and subsequently maximize their agreement in the representation space. However, these methods often neglect the rationality of the augmented samples. Due to significant uncertainty, random augmentation can disrupt the semantic information and interest evolution patterns inherent in the original user sequences. Moreover, pulling semantically inconsistent sequences closer in the representation space can render the user sequence embeddings insensitive to variations in user preferences, which contradicts the primary objective of sequential recommendation. To address these limitations, we propose the Context-aware Diffusion-based Contrastive Learning for Sequential Recommendation, named CaDiRec. The core idea is to leverage context information to generate more reasonable augmented views. Specifically, CaDiRec employs a context-aware diffusion model to generate alternative items for the given positions within a sequence. These generated items are aligned with their respective context information and can effectively replace the corresponding original items, thereby generating a positive view of the original sequence. By considering two different augmentations of the same user sequence, we can construct a pair of positive samples for contrastive learning. To ensure representation cohesion, we train the entire framework in an end-to-end manner, with shared item embeddings between the diffusion model and the recommendation model. Extensive experiments on five benchmark datasets demonstrate the advantages of our proposed method over existing baselines.

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