Algorithms For Cold-Start Game Recommendation Based On GNN Pre-training Model

Abstract In the absence of sufficient user behavior data, game recommendation systems face the cold-start problem. To address this issue, this paper proposes a solution based on the Graph Neural Network pre-training model to alleviate the cold-start problem. The proposed model directly reconstructs cold-start user/game embeddings using a meta-learning setup based on dataset training simulations and uses an adaptive neighbor sampler to improve user interaction relations and thereby to improve game recommendation performance. Experimental results demonstrate the effectiveness and practicality of the recommendation model proposed in this study. Moreover, the proposed model is embedded in the game recommendation system to visualize the recommendation results.