Debiased Contrastive Representation Learning for Mitigating Dual Biases in Recommender Systems
This addresses biases that undermine recommender effectiveness for users, but it is incremental as it builds on existing contrastive learning methods.
The paper tackles the problem of popularity and conformity biases in recommender systems, which cause over-representation of popular items and unbalanced data, by proposing a Debiased Contrastive Learning framework (DCLMDB) that reduces these biases and improves recommendation accuracy and diversity on Movielens-10M and Netflix datasets.
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. We construct a causal graph to address both biases and describe the abstract data generation mechanism. Then, we use it as a guide to develop a novel Debiased Contrastive Learning framework for Mitigating Dual Biases, called DCLMDB. In DCLMDB, both popularity bias and conformity bias are handled in the model training process by contrastive learning to ensure that user choices and recommended items are not unduly influenced by conformity and popularity. Extensive experiments on two real-world datasets, Movielens-10M and Netflix, show that DCLMDB can effectively reduce the dual biases, as well as significantly enhance the accuracy and diversity of recommendations.