Integrating direct and indirect views for group recommendation: An inter- and intra-view contrastive learning method

Abstract

The growing popularity of online social networking has made it increasingly important to develop group recommender systems (RS) for delivering personalized services to the members of user groups. However, owing to the sparsity of data on group–item interactions (G–I interactions), existing group recommendation methods have concentrated on modeling user–item interactions (U–I interactions), which has limited the validity of the extracted group preferences. We propose a novel inter- and intra-view contrastive learning (I2VC) method for group recommendation, focusing on combining the direct view concerning group–item records and the indirect view concerning user–item records. The proposed method features a contrastive learning mechanism that incorporates two strategies (i.e., inter-view learning and intra-view learning) to overcome challenges in achieving the cross-view matching of the same group and the within-view discrimination among different groups. We empirically evaluate the proposed method using two real-world datasets. The results show that our method is more effective than other group recommendation methods. In addition, our findings show that the I2VC method is capable of boosting the alignment of strongly correlated group embeddings and the dispersion of weakly correlated ones, further demonstrating its effectiveness in view collaboration.