Personalized Item Representations in Federated Multimodal Recommendation

• URL: http://arxiv.org/abs/2410.08478v2
• Date: Mon, 14 Oct 2024 07:55:16 GMT
• Title: Personalized Item Representations in Federated Multimodal Recommendation
• Authors: Zhiwei Li, Guodong Long, Jing Jiang, Chengqi Zhang,
• Abstract summary: Federated Multimodal Recommendation System, called FedMR, encodes multimodal item data. FedMR is compatible with existing ID-based federated recommendation systems. Experiments on four real-world multimodal datasets demonstrate FedMR's effectiveness.
• Score: 37.52127488593226
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated recommendation systems are essential for providing personalized recommendations while protecting user privacy. However, current methods mainly rely on ID-based item embeddings, neglecting the rich multimodal information of items. To address this, we propose a Federated Multimodal Recommendation System, called FedMR. FedMR uses a foundation model on the server to encode multimodal item data, such as images and text. To handle data heterogeneity caused by user preference differences, FedMR introduces a Mixing Feature Fusion Module on each client, which adjusts fusion strategy weights based on user interaction history to generate personalized item representations that capture users' fine-grained preferences. FedMR is compatible with existing ID-based federated recommendation systems, improving performance without modifying the original framework. Experiments on four real-world multimodal datasets demonstrate FedMR's effectiveness. The code is available at https://anonymous.4open.science/r/FedMR.

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