Causal Embeddings for Recommendation: An Extended Abstract

Recommendations are commonly used to modify user’s natural behavior, for example, increasing product sales or the time spent on a website. This results in a gap between the ultimate business ob- jective and the classical setup where recommenda- tions are optimized to be coherent with past user be- havior. To bridge this gap, we propose a new learn- ing setup for recommendation that optimizes for the Incremental Treatment Effect (ITE) of the policy. We show this is equivalent to learning to predict recommendation outcomes under a fully random recommendation policy and propose a new domain adaptation algorithm that learns from logged data containing outcomes from a biased recommenda- tion policy and predicts recommendation outcomes according to random exposure. We compare our method against state-of-the-art factorization meth- ods, in addition to new approaches of causal rec- ommendation and show significant improvements.