Tackling Interference Induced by Data Training Loops in A/B Tests: A Weighted Training Approach

In modern recommendation systems, the standard pipeline involves training machine learning models on historical data to predict user behaviors and improve recommendations continuously. However, these data training loops can introduce interference in A/B tests, where data generated by control and treatment algorithms, potentially with different distributions, are combined. To address these challenges, we introduce a novel approach called weighted training. This approach entails training a model to predict the probability of each data point appearing in either the treatment or …

algorithms control cs.lg data econ.em generated historical data interference machine machine learning machine learning models modern pipeline recommendation recommendations recommendation systems standard stat.me systems tests training treatment