Unbiased Sequential Recommendation with Latent Confounders

Sequential recommendation holds the promise of understanding user preference by capturing successive behavior correlations. Existing research focus on designing different models for better fitting the offline datasets. However, the observational data may have been contaminated by the exposure or selection biases, which renders the learned sequential models unreliable. In order to solve this fundamental problem, in this paper, we propose to reformulate the sequential recommendation task with the potential outcome framework, where we are able to clearly understand the data bias mechanism and correct it by re-weighting the training instances with the inverse propensity score (IPS). For more robustness modeling, a clipping strategy is applied to the IPS estimation to reduce the variance of the learning objective. To make our framework more practical, we design a parameterized model to remove the impact of the potential latent confounders. At last, we theoretically analyze the unbiasedness of the proposed framework under both vanilla and clipping IPS estimations. To the best of our knowledge, this is the first work on debiased sequential recommendation. We conduct extensive experiment based on both synthetic and real-world datasets to demonstrate the effectiveness of our framework.