Rethinking Contrastive Learning in Session-based Recommendation

TL;DR

This paper introduces MACL, a multi-modal adaptive contrastive learning framework that enhances session-based recommendation by generating semantically consistent views and weighting signals adaptively, outperforming existing methods.

Contribution

The paper proposes a novel multi-modal augmentation and an adaptive contrastive loss for better session-based recommendation, addressing key limitations of prior contrastive learning approaches.

Findings

MACL outperforms state-of-the-art methods on three datasets.

Multi-modal augmentation improves semantic consistency.

Adaptive contrastive loss enhances learning effectiveness.

Abstract

Session-based recommendation aims to predict intents of anonymous users based on limited behaviors. With the ability in alleviating data sparsity, contrastive learning is prevailing in the task. However, we spot that existing contrastive learning based methods still suffer from three obstacles: (1) they overlook item-level sparsity and primarily focus on session-level sparsity; (2) they typically augment sessions using item IDs like crop, mask and reorder, failing to ensure the semantic consistency of augmented views; (3) they treat all positive-negative signals equally, without considering their varying utility. To this end, we propose a novel multi-modal adaptive contrastive learning framework called MACL for session-based recommendation. In MACL, a multi-modal augmentation is devised to generate semantically consistent views at both item and session levels by leveraging item multi-modal features. Besides, we present an adaptive contrastive loss that distinguishes varying contributions of positive-negative signals to improve self-supervised learning. Extensive experiments on three real-world datasets demonstrate the superiority of MACL over state-of-the-art methods.