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Causal Embeddings for Recommendation


Recommendations are treatments. While todays recommender systems attempt to emulate the naturally occurring user behaviour by predicting either missing entries in the user-item matrix or computing the most likely continuation of user sessions, we need to start thinking of recommendations in terms of optimal interventions with respect to specific goals, such as the increase of number of user conversions on a E-Commerce website. This objective is known as Incremental Treatment Effect prediction (ITE) in the causal community. We propose a new way of factorizing user-item matrices created from a large sample of biased data collected using a control recommendation policy and from limited randomized recommendation data collected using a treatment recommendation policy in order to jointly optimize the prediction of outcomes of the treatment policy and its incremental treatment effect with respect to the control policy. We compare our method against both state-of-the-art factorization methods and against new approaches of causal recommendation and show significant improvements in performance.