Abstract The video game industry has grown rapidly, driven by increased online gaming engagement and diverse game genres. To help users discover potential games amid data overload, effective recommendation models are essential for online platforms. Although recent research addresses cold start issues by integrating multimodal contents, current approaches focus on the implicit modeling of collaborative connections between items and users, neglecting latent semantic associations within content attributes. Additionally, they fail to effectively integrate diverse modalities, relying on simplistic fusion techniques that overlook intricate content. This paper proposed a novel generative AI-augmented multimodal graph-based recommender with parallel-stage contrastive learning framework (GAIMG-CL), constructs the latent semantic item-item and user-user relationships based on image and textual data and incorporates the concept of multimodal contrastive learning. By leveraging modality-aware latent relationships, graph convolutions are employed to integrate both item and user correlations into explicit modality-aware representations. Moreover, our innovative contrastive learning framework enhances fine-grained multimodal fusion. We also leverage GAI for valuable information extraction, enhancing representation learning quality. This approach also enables the integration of enhanced item and user representations into collaborative filtering techniques. Our proposed approach outperforms other recommendation methods and notably boosts cold start recommendation performance, as evidenced by the experimental results.
Huang et al. (Mon,) studied this question.