Cross-Modally Aligned and Temporally Gated Mixture of Experts for Multimodal Sequential Recommendation
This work proposes a multimodal sequential recommendation framework with cross-modal alignment and temporal gating, which leverages item ID, text, and image modalities to capture users’ dynamic interests and demonstrates that the proposed method consistently outperforms representative sequential and multimodal recommen...