M^2VAE: Multi-Modal Multi-View Variational Autoencoder for Cold-start Item Recommendation
- URL: http://arxiv.org/abs/2508.00452v1
- Date: Fri, 01 Aug 2025 09:16:26 GMT
- Title: M^2VAE: Multi-Modal Multi-View Variational Autoencoder for Cold-start Item Recommendation
- Authors: Chuan He, Yongchao Liu, Qiang Li, Wenliang Zhong, Chuntao Hong, Xinwei Yao,
- Abstract summary: Cold-start item recommendation is a significant challenge in recommendation systems.<n>Existing methods leverage multi-modal content to alleviate the cold-start issue.<n>We propose a generative model that addresses the challenges of modeling common and unique views in attribute and multi-modal features.
- Score: 14.644213412218742
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Cold-start item recommendation is a significant challenge in recommendation systems, particularly when new items are introduced without any historical interaction data. While existing methods leverage multi-modal content to alleviate the cold-start issue, they often neglect the inherent multi-view structure of modalities, the distinction between shared and modality-specific features. In this paper, we propose Multi-Modal Multi-View Variational AutoEncoder (M^2VAE), a generative model that addresses the challenges of modeling common and unique views in attribute and multi-modal features, as well as user preferences over single-typed item features. Specifically, we generate type-specific latent variables for item IDs, categorical attributes, and image features, and use Product-of-Experts (PoE) to derive a common representation. A disentangled contrastive loss decouples the common view from unique views while preserving feature informativeness. To model user inclinations, we employ a preference-guided Mixture-of-Experts (MoE) to adaptively fuse representations. We further incorporate co-occurrence signals via contrastive learning, eliminating the need for pretraining. Extensive experiments on real-world datasets validate the effectiveness of our approach.
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