Although pretraining has garnered significant attention and popularity in recent years, its application in graph-based recommender systems is relatively limited. It is challenging to exploit prior knowledge by pretraining in widely used ID-dependent datasets. On one hand, user-item interaction history in one dataset can hardly be transferred to other datasets through pretraining, where IDs are different. On the other hand, pretraining and finetuning on the same dataset leads to a high risk of overfitting. In this paper, we propose a novel multitask pretraining framework named Unified Pretraining for Recommendation via Task Hypergraphs. For a unified learning pattern to handle diverse requirements and nuances of various pretext tasks, we design task hypergraphs to generalize pretext tasks to hyperedge prediction. A novel transitional attention layer is devised to discriminatively learn the relevance between each pretext task and recommendation. Experimental results on three benchmark datasets verify the superiority of UPRTH. Additional detailed investigations are conducted to demonstrate the effectiveness of the proposed framework.

翻译：尽管预训练技术近年来备受关注并广泛流行，但其在基于图的推荐系统中的应用仍相对有限。在广泛使用的ID依赖型数据集中，通过预训练利用先验知识面临挑战：一方面，不同数据集中的用户-商品交互历史因ID差异而难以通过预训练实现迁移；另一方面，在同一数据集上进行预训练和微调会带来较高的过拟合风险。本文提出了一种名为“基于任务超图的统一预训练推荐框架”的新型多任务预训练框架。为实现统一学习模式以应对不同预训练任务的多样化需求和细微差异，我们设计了任务超图将预训练任务泛化为超边预测问题。同时，创新性地提出了过渡注意力层，用于判别式学习各预训练任务与推荐任务之间的相关性。在三个基准数据集上的实验验证了UPRTH的优越性，并通过详细消融研究进一步证明了该框架的有效性。