XsimGCL’s cross-layer for group recommendation using extremely simple graph contrastive learning

Group recommendation involves suggesting items or activities to a group of users based on their collective preferences or characteristics. Graph contrastive learning is a technique used to learn representations of items and users in a graph structure. Although contrastive learning-based recommendation techniques reduce the data sparsity problem by extracting general features from raw data and also make the representation of user-item bipartite graph augmentations more consistent, the factors contributing to improving the performance of this technique are still not fully understood. Meanwhile, graph augmentations have little importance in contrastive learning-based recommendation and are relatively unreliable. The eXtremely Simple Graph Contrastive Learning (XSimGCL) provides novel insights into the effect of contrastive learning on recommendation, where views for contrastive learning are created through a simple yet effective noise-based embedding augmentation. Although XSimGCL infers the final group decision by dynamically aggregating the preferences of group members and includes various types of interaction, the performance of supervised learning is reduced due to the data sparsity problem, and as a result, the efficiency of group preference representation is limited. To address this challenge, we developed a Group Recommendation model based on XsimGCL in this study (GR-GCL). GR-GCL is inspired by the Light Graph Convolution Network (LightGCN) to realize simultaneous learning of multiple graphs, where initial embedding is considered the only update parameter. Also, GR-GCL improves group recommendation by applying cross-layer contrastive learning in the XSimGCL model by representing more diverse entities. The rationality analysis of our proposed GR-GCL has been performed on several datasets from both analytical and empirical perspectives. Although our model is very simple, it performs better in group recommendations by adjusting the uniformity of representations learned from counterparts based on contrastive learning.