A Deep Generative Recommendation Method for Unbiased Learning from Implicit Feedback
Shashank Gupta, Harrie Oosterhuis, and Maarten de Rijke Published in Proceedings of the 2023 ACM SIGIR International Conference on Theory of Information Retrieval 2023. (ICTIR ’23), 2023. [pdf, code]
Variational autoencoders (VAEs) are the state-of-the-art model for recommendation with implicit feedback signals. Unfortunately, implicit feedback suffers from selection bias, e.g., popularity bias, position bias, etc., and as a result, training from such signals produces biased recommendation models. Existing methods for debiasing the learning process have not been applied in a generative setting.
We address this gap by introducing an inverse propensity scoring (IPS) based method for training VAEs from implicit feedback data in an unbiased way. Our IPS-based estimator for the VAE training objective, VAE-IPS, is provably unbiased w.r.t. selection bias. Our experimental results show that the proposed VAE-IPS model reaches significantly higher performance than existing baselines. Our contributions enable practitioners to combine state-of-the-art VAE recommendation techniques with the advantages of bias mitigation for implicit feedback.
Recommended citation:
Gupta, S., Oosterhuis, H., & de Rijke, M. (2023, August). A Deep Generative Recommendation Method for Unbiased Learning from Implicit Feedback. In Proceedings of the 2023 ACM SIGIR International Conference on Theory of Information Retrieval (pp. 87-93).