Self‐supervised graph learning for occasional group recommendation

Abstract

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. 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.

Enhancing group recommender systems: A fusion of social tagging and collaborative filtering for cohesive recommendations

“…Moreover, there have been many recent advances in group recommendations, a common thread running through these studies is that collaborative filtering and user relationships are major factors to consider when building models (Castro et al, 2018; Hao et al, 2022; Morawski et al, 2017; Pérez‐Almaguer et al, 2021; Seo et al, 2018). It is also possible to recognize other hybrid developments that focus on effectively combining project characteristics into models based on collaborative filtering.…”