As an important branch in Recommender System, occasional group recommendation
has received more and more attention. In this scenario, each occasional group
(cold-start group) has no or few historical interacted items. As each
occasional group has extremely sparse interactions with items, traditional
group recommendation methods can not learn high-quality group representations.
The recent proposed Graph Neural Networks (GNNs), which incorporate the
high-order neighbors of the target occasional group, can alleviate the above
problem in some extent. However, these GNNs still can not explicitly strengthen
the embedding quality of the high-order neighbors with few interactions.
Motivated by the Self-supervised Learning technique, which is able to find the
correlations within the data itself, we propose a self-supervised graph
learning framework, which takes the user/item/group embedding reconstruction as
the pretext task to enhance the embeddings of the cold-start
users/items/groups. In order to explicitly enhance the high-order cold-start
neighbors' embedding quality, we further introduce an embedding enhancer, which
leverages the self-attention mechanism to improve the embedding quality for
them. Comprehensive experiments show the advantages of our proposed framework
than the state-of-the-art methods.

提出了一种利用自监督学习技术的图学习框架来提高稀有组的嵌入质量，以解决传统的组推荐方法在冷启动组中表现不佳的问题。实验结果表明，该框架优于当前最先进的方法。