A personalized recommendation model with multimodal preference-based graph attention network

Graph neural networks (GNNs) have indeed shown significant potential in the field of personalized recommendation. The core approach is to reorganize interaction data into a user-item bipartite graph, leveraging high-order connectivity between user and item nodes to enhance their representations. However, most existing methods only deploy graph neural networks on parallel interaction graphs and treat the information propagated from all neighbors as equivalent, failing to adaptively capture user preferences. Therefore, the representations obtained may contain redundant or even noisy information, leading to non-robustness and suboptimal performance. The recently proposed Multimodal Graph Attention Network (MGAT) disentangles personal interests at the granularity of modality, operating on individual modal interaction graphs while utilizing a gated attention mechanism to differentiate the impacts of different modalities on user preferences. However, MGAT merely uses averaging to fuse multimodal features, which might overlook unique or critical information within each modality. To address this issue, this paper proposes a multimodal preference-based graph attention network. Firstly, for each individual modality, a single-modality graph network is constructed by integrating the user-item interaction bipartite graph, enabling it to learn user preferences for that modality. GNNs are used to aggregate neighborhood information and enhance the representation of each node. Additionally, a GRU module is utilized to determine whether to aggregate neighborhood information, thereby achieving noise reduction. In addition, a lightweight complementary attention mechanism is proposed to fuse user and item representations learned from different modal graphs. The complementary attention mechanism can not only avoid information redundancy, but also alleviate the problem of modal loss to a certain extent, and ultimately input the fusion results into the prediction module. Experimental results on the MovieLens and TikTok datasets demonstrate the effectiveness of the multimodal information and attention fusion mechanism in improving recommendation accuracy. Compared to baseline state-of-the-art algorithms, the proposed model achieves significant improvements in the Precision@K, Recall@K, NDCG@K and AUC metrics. Our code is publicly available on GitHub: [https://github.com/Oasisway624/mgpat].