Mitigating Latent User Biases in Pre-trained VAE Recommendation Models via On-demand Input Space Transformation

Recommender systems can unintentionally encode protected attributes (e.g., gender, country, or age) in their learned latent user representations. Current in-processing debiasing approaches, notably adversarial training, effectively reduce the encoded information on private user attributes. These approaches modify the model parameters during training. Thus, to alternate between biased and debiased model, two separate models have to be trained. In contrast, we propose a novel method to debias recommendation models post-training, which allows switching between biased and debiased model at inference time. Focusing on state-of-the-art variational autoencoder (VAE) architectures, our method aims to reduce bias at input level (user–item interactions) by learning a transformation from input space to a debiased subspace. As the output of this transformation lies in the same space as the original input vector, we can use transformed (debiased) input vectors without the need to fine-tune the pre-trained model. We evaluate the effectiveness of our method on three datasets, MovieLens-1M, LFM2b-DemoBias, and EB-NeRD, from the movie, music, and news domains, respectively. Our experiments show that the proposed method achieves task performance (in terms of NDCG) and debiasing strength (in terms of balanced accuracy of an attacker network) that are comparable to applying adversarial training during the initial training procedure, while providing the added functionality of alternating between biased and debiased model at inference time.