Graph Neural Network (GNN)-based models have become the mainstream approach for recommender systems. Despite the effectiveness, they are still suffering from the cold-start problem, i.e., recommend for few-interaction items. Existing GNN-based recommendation models to address the cold-start problem mainly focus on utilizing auxiliary features of users and items, leaving the user-item interactions under-utilized. However, embeddings distributions of cold and warm items are still largely different, since cold items' embeddings are learned from lower-popularity interactions, while warm items' embeddings are from higher-popularity interactions. Thus, there is a seesaw phenomenon, where the recommendation performance for the cold and warm items cannot be improved simultaneously. To this end, we proposed a Uncertainty-aware Consistency learning framework for Cold-start item recommendation (shorten as UCC) solely based on user-item interactions. Under this framework, we train the teacher model (generator) and student model (recommender) with consistency learning, to ensure the cold items with additionally generated low-uncertainty interactions can have similar distribution with the warm items. Therefore, the proposed framework improves the recommendation of cold and warm items at the same time, without hurting any one of them. Extensive experiments on benchmark datasets demonstrate that our proposed method significantly outperforms state-of-the-art methods on both warm and cold items, with an average performance improvement of 27.6%.

翻译：基于图神经网络（GNN）的模型已成为推荐系统的主流方法。尽管效果显著，但此类模型仍面临冷启动问题，即难以对具有少量交互的物品进行推荐。现有解决冷启动问题的GNN推荐模型主要侧重于利用用户和物品的辅助特征，而对用户-物品交互信息的利用不足。然而，冷启动物品与热门物品的嵌入分布仍存在显著差异，因为冷启动物品的嵌入是从低流行度交互中学习得到的，而热门物品的嵌入则来源于高流行度交互。因此，存在一种“跷跷板”现象：冷启动物品与热门物品的推荐性能无法同时提升。为此，本文提出了一种仅基于用户-物品交互的不确定性感知一致性学习框架（简称UCC），用于冷启动物品推荐。在该框架下，我们通过一致性学习训练教师模型（生成器）与学生模型（推荐器），以确保额外生成的低不确定性交互的冷启动物品能够与热门物品具有相似的分布。因此，所提框架能够在不对任何一方造成损害的前提下，同时提升冷启动物品与热门物品的推荐效果。在基准数据集上的大量实验表明，我们的方法在冷启动物品与热门物品上均显著优于现有最先进方法，平均性能提升达27.6%。