Bayesian Value Based Recommendation: A modelling based alternative to proxy and counterfactual policy based recommendation

We develop the value based approach to Recommender systems. The value approach is a model based approach that allows forecasting of actual A/B test performance. It contrasts with the proxy based approach, which attempt to order the performance of different recommendation systems, but not forecast actual performance. It also contrasts with policy based approaches which also produce a performance forecast but use propensity scores to by-pass the requirement for a model. Value based approaches are a state of the art approach for combining organic and bandit signals that can utilise the three fundamental distances of recommendation. Their deployment requires sophisticated modelling and Bayesian computation. This tutorial develops the theory of value based recommendation and demonstrates the approach with examples in python notebooks.