Sequences as Nodes for Contrastive Multimodal Graph Recommendation

TL;DR

MuSICRec is a graph-based recommender system that effectively combines multimodal, sequential, and collaborative signals using contrastive learning, improving recommendations especially for users with limited interaction history.

Contribution

The paper introduces MuSICRec, a novel multi-view graph model that organically incorporates sequential context and modulates multimodal features to enhance recommendation accuracy.

Findings

Outperforms state-of-the-art baselines on multiple datasets.

Achieves significant improvements for users with short interaction histories.

Effectively mitigates cold-start and data sparsity issues.

Abstract

To tackle cold-start and data sparsity issues in recommender systems, numerous multimodal, sequential, and contrastive techniques have been proposed. While these augmentations can boost recommendation performance, they tend to add noise and disrupt useful semantics. To address this, we propose MuSICRec (Multimodal Sequence-Item Contrastive Recommender), a multi-view graph-based recommender that combines collaborative, sequential, and multimodal signals. We build a sequence-item (SI) view by attention pooling over the user's interacted items to form sequence nodes. We propagate over the SI graph, obtaining a second view organically as an alternative to artificial data augmentation, while simultaneously injecting sequential context signals. Additionally, to mitigate modality noise and align the multimodal information, the contribution of text and visual features is modulated according to an ID-guided gate. We evaluate under a strict leave-two-out split against a broad range of sequential, multimodal, and contrastive baselines. On the Amazon Baby, Sports, and Electronics datasets, MuSICRec outperforms state-of-the-art baselines across all model types. We observe the largest gains for short-history users, mitigating sparsity and cold-start challenges. Our code is available at https://anonymous.4open.science/r/MuSICRec-3CEE/ and will be made publicly available.