Debiased Contrastive Representation Learning for Mitigating Dual Biases in Recommender Systems

In recommender systems, popularity and conformity biases undermine recommender effectiveness by disproportionately favouring popular items, leading to their over-representation in recommendation lists and causing an unbalanced distribution of user-item historical data. In this research, a causal graph is constructed to address biases and describe the abstract data generation mechanism. The causal graph is then used as a guide to develop a novel Debiased Contrastive Learning framework for Mitigating Dual Biases, referred to as DCLMDB. In the DCLMDB, both popularity bias and conformity bias are handled during the model training process through contrastive learning, ensuring that user choices and recommended items are not unduly influenced by conformity or popularity. Extensive experiments on three real-world datasets, Movielens-10M, Netflix and Amazon-Art, demonstrate that DCLMDB effectively reduces dual biases. Specifically, on the Movielens-10M dataset, DCLMDB achieves a 35.11% improvement in Recall@20 compared to the backbone model, and significantly outperforms the second-best baseline.