Multi-modal Adaptive Mixture of Experts for Cold-start Recommendation

• URL: http://arxiv.org/abs/2508.08042v1
• Date: Mon, 11 Aug 2025 14:47:14 GMT
• Title: Multi-modal Adaptive Mixture of Experts for Cold-start Recommendation
• Authors: Van-Khang Nguyen, Duc-Hoang Pham, Huy-Son Nguyen, Cam-Van Thi Nguyen, Hoang-Quynh Le, Duc-Trong Le,
• Abstract summary: MAMEX is a novel framework for multimodal cold-start recommendation.<n>It dynamically leverages latent representation from different modalities.<n>Experiments show MAMEX outperforms state-of-the-art methods in cold-start scenarios.
• Score: 1.9967512860886603
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Recommendation systems have faced significant challenges in cold-start scenarios, where new items with a limited history of interaction need to be effectively recommended to users. Though multimodal data (e.g., images, text, audio, etc.) offer rich information to address this issue, existing approaches often employ simplistic integration methods such as concatenation, average pooling, or fixed weighting schemes, which fail to capture the complex relationships between modalities. Our study proposes a novel Mixture of Experts (MoE) framework for multimodal cold-start recommendation, named MAMEX, which dynamically leverages latent representation from different modalities. MAMEX utilizes modality-specific expert networks and introduces a learnable gating mechanism that adaptively weights the contribution of each modality based on its content characteristics. This approach enables MAMEX to emphasize the most informative modalities for each item while maintaining robustness when certain modalities are less relevant or missing. Extensive experiments on benchmark datasets show that MAMEX outperforms state-of-the-art methods in cold-start scenarios, with superior accuracy and adaptability. For reproducibility, the code has been made available on Github https://github.com/L2R-UET/MAMEX.

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