A fairness-aware graph contrastive learning recommender framework for social tagging systems

Abstract

Personalized recommendations for social tagging systems aim to deliver high-quality recommendations for users and annotate meaningful characteristics on items. However, compared to some state-of-the-art general recommender systems, current tag-aware recommender systems (TRS) have no advantage in either performance or efficiency. We discover that tagging information not only boost performance, but also promote fairness caused by biased data. In our work, we propose a fairness-aware graph contrastive learning framework named Tag-aware Graph Contrastive Learning (TAGCL). The design of its graph structure is composed of two bipartite graphs: user-tag graph and item-tag graph. Leveraging contrastive learning paradigm, our proposed TAGCL is able to encode stable and high-quality representations. In the fairness-aware learning, we jointly optimize the model through negative tag learning and TransT regularization. Extensive experiments on three public datasets and two sizable real-world datasets show that TAGCL outperforms some state-of-the-art general recommender methods and TRS in terms of accuracy of recommendations and dramatically reduces bias on some heavily skewed dataset. We also carry out comprehensive ablation studies to verify the validity of our proposed framework.