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.

我们提出了一种名为 Unified Pretraining for Recommendation via Task Hypergraphs 的新型多任务预训练框架，通过任务超图将预训练任务广泛应用于推荐系统中，设计了一种新颖的过渡关注层来区分性地学习每个预训练任务和推荐之间的相关性，并通过实验结果验证了 UPRTH 的优越性，并进行了进一步的详细研究来证明该框架的有效性。