Generative Adversarial Networks (GANs) have several advantages over latent variable and autoregressive models, such as directly computing the input data distribution, fast sampling, and best samples generated. Furthermore, the metadata information of users and items plays a crucial role in effectively learning the user-item latent representations. Existing neural network approaches are deterministic, incapable of accounting for user-item latent representations, and the rating prediction process is calculative rather than generative. However, only some papers have utilized GANs for information retrieval tasks. However, they have several limitations, such as the inability to abstract data effectively, the limitation of the model's ability to learn accurate latent representations, and the absence of metadata information. To address these issues, we propose a GAN-based recommendation framework named Collaborative Filtering with Metadata-awareGenerative Adversarial Networks (CF-MGAN) to learn complex rating distribution for collaborative filtering (CF) tasks. We use a negative item generator for the implicit user purchase vector to prevent GAN from generating biased ratings. We exploited metadata information to overcome data sparsity issues. We use Variational Autoencoder to abstract user-item metadata features, a regularizer for GAN-generated rating. Extensive experiments on datasets reveal the effectiveness of the proposed model, showing an improvement in the top-N recommendation task.
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