M^2VAE: Multi-Modal Multi-View Variational Autoencoder for Cold-start Item Recommendation
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915612521922560 |
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| author | He, Chuan Liu, Yongchao Li, Qiang Zhong, Wenliang Hong, Chuntao Yao, Xinwei |
| author_facet | He, Chuan Liu, Yongchao Li, Qiang Zhong, Wenliang Hong, Chuntao Yao, Xinwei |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2508_00452 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | M^2VAE: Multi-Modal Multi-View Variational Autoencoder for Cold-start Item Recommendation He, Chuan Liu, Yongchao Li, Qiang Zhong, Wenliang Hong, Chuntao Yao, Xinwei Information Retrieval Artificial Intelligence 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. |
| title | M^2VAE: Multi-Modal Multi-View Variational Autoencoder for Cold-start Item Recommendation |
| topic | Information Retrieval Artificial Intelligence |
| url | https://arxiv.org/abs/2508.00452 |