The surge in the number of users and games in online game communities (e.g., Steam) has resulted in significant information overloading. With a vast array of games available, it becomes challenging for users to find ones they like. Existing recommendation methods are mostly designed for the fields of e-commerce and news, leaving the demand for game recommendation under-served. In this paper, we bridge this important gap by proposing the Multimodal contrastive learning with Hyperbolic geometry for KG-based Game Recommendation (McHKGR). Given that game items are multimodal (e.g., visual images, textual captions) and include numerous attributes, we construct a knowledge graph (KG) to store the complex interaction patterns between users and games as well as their multimodal features. Unlike existing methods that rely on Euclidean spaces or modality-agnostic representations, McHKGR encodes modality-specific views in hyperbolic space, enabling more expressive semantic alignment and better preservation of hierarchical user-game structures. Furthermore, we design a user co-occurrence graph with virtual relations based on the number of co-interaction items to enhance user representation. To bridge modality gaps, we also introduce a cross-modal contrastive learning strategy that unifies heterogeneous signals across modalities. Extensive experiments on our constructed real-world dataset Steam and public dataset MovieLens demonstrate that McHKGR outperforms fourteen state-of-the-art baselines, achieving up to 1.83% and 52.28% improvement in AUC and Recall@20 on Steam, and up to 1.48% and 45.23% improvement in AUC and Recall@20 on Movielens, respectively.
