# Causal Embeddings for Recommendation

## TL;DR

This paper introduces a causal embedding approach for recommendation systems that optimizes for desired outcomes by learning from biased logged data, bridging the gap between traditional relevance metrics and actual business goals.

## Contribution

The paper proposes a novel domain adaptation algorithm that predicts recommendation outcomes under random exposure using logged data from biased policies.

## Key findings

- Significant improvement over state-of-the-art factorization methods.
- Effective learning from biased logged data for outcome prediction.
- Enhanced recommendation policy optimization for business objectives.

## Abstract

Many current applications use recommendations in order to modify the natural user behavior, such as to increase the number of sales or the time spent on a website. This results in a gap between the final recommendation objective and the classical setup where recommendation candidates are evaluated by their coherence with past user behavior, by predicting either the missing entries in the user-item matrix, or the most likely next event. To bridge this gap, we optimize a recommendation policy for the task of increasing the desired outcome versus the organic user behavior. We show this is equivalent to learning to predict recommendation outcomes under a fully random recommendation policy. To this end, we propose a new domain adaptation algorithm that learns from logged data containing outcomes from a biased recommendation policy and predicts recommendation outcomes according to random exposure. We compare our method against state-of-the-art factorization methods, in addition to new approaches of causal recommendation and show significant improvements.