Counterfactual evaluation for recommendation systems (eugeneyan.com)

A new approach to evaluating recommendation systems has emerged through the concept of counterfactual evaluation, which redefines how recommendations are measured. Traditionally, offline evaluations utilized static historical data, treating recommendations as observational problems akin to supervised machine learning. However, this method neglects the interventional nature of recommendations, which directly influence customer behavior, such as clicks and purchases. By employing counterfactual evaluation methods, particularly Inverse Propensity Scoring (IPS), data scientists can estimate the effects of potential recommendations without needing to run lengthy A/B tests. This shift could yield more reliable insights into recommendation efficacy, allowing businesses to better predict user interactions with different recommendation strategies. Counterfactual evaluation, especially through methods like SNIPS (Self-Normalized Inverse Propensity Scoring), shows great promise by allowing practitioners to simulate A/B tests offline, thereby enhancing the accuracy of their models based on logged interactions. While SNIPS offers improved performance over traditional IPS without heavy parameter tuning, it does come with increased computational demands, particularly when dealing with non-zero rewards. The ongoing discussion highlights the limitations of current methods while advocating for integration with existing offline frameworks, providing a balanced pathway for future research and development in the AI/ML community. This novel perspective on recommendation system evaluation could significantly impact how businesses engage with users and optimize their algorithms for better decision-making.