In this talk, I will present Machine Learning solutions for three specific recommendation system challenges in the real world –

• Node recommendations in directed graphs: Given a directed graph, the problem is to recommend the top-k nodes with the highest likelihood of a link from a query node. We enhance GNNs with dual embeddings and propose adaptive neighborhood sampling techniques to handle asymmetric recommendations.
• Delayed feedback: The problem is to train an ML model in the presence of target labels that may change over time due to delayed feedback of user actions. We employ an importance sampling strategy to deal with delayed feedback – the strategy corrects the bias in both target labels and feature computation, and leverages pre-conversion signals such as clicks.
• Uncertainty in model predictions: For binary classification problems, we show that we can leverage uncertainty estimates for model predictions to improve accuracy. Specifically, we propose algorithms to select decision boundaries with multiple threshold values on model scores, one per uncertainty level, to increase recall without hurting precision.