Sequential Modality Dropout for Robust Multi-Modal Sequential Recommendation
Multi-modal sequential recommenders assume every item carries every modality, but real product catalogs often miss images or text, and a model trained on complete data loses much of its recommendation accuracy when a modality is unavailable at serving time. We propose Sequential Modality Dropout (SMD): during training, each modality stream (image and text) is independently erased with probability p for an entire user interaction history, so the model learns to predict the next item without relying on any single modality. We measure robustness by retention, the fraction of a model's full-modality accuracy (HR@10) that survives when a modality is removed at test time. Across four backbones ...
| Authors | Guanqun Yang, Wenlong Zhang |
|---|---|
| Primary category | cs.IR |
| Published | 10 August 2026 |
| Last updated at source | 10 August 2026 |
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