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.

翻译：为解决推荐系统中的冷启动和数据稀疏性问题，众多多模态、序列化及对比学习技术被提出。尽管这些增强方法能够提升推荐性能，但它们往往会引入噪声并破坏有用的语义信息。为此，我们提出MuSICRec（多模态序列-项目对比推荐器），一种基于多视图图的推荐模型，它融合了协同过滤、序列模式及多模态信号。我们通过注意力池化对用户交互过的项目进行聚合以构建序列节点，从而形成序列-项目（SI）视图。通过在SI图上进行信息传播，我们自然地获得第二个视图，以此替代人工数据增强，同时注入序列上下文信号。此外，为减轻模态噪声并实现多模态信息对齐，文本与视觉特征的贡献度通过一个ID引导的门控机制进行调节。我们在严格的留二划分设置下，与广泛的序列化、多模态及对比学习基线模型进行了比较评估。在Amazon Baby、Sports和Electronics数据集上，MuSICRec在所有模型类型中均优于当前最先进的基线方法。我们观察到该模型对交互历史较短的用户提升最为显著，有效缓解了数据稀疏性与冷启动的挑战。我们的代码发布于https://anonymous.4open.science/r/MuSICRec-3CEE/并将公开提供。