Causality-Aware Neighborhood Methods for Recommender Systems
This work provides a more robust method for recommender systems to achieve business objectives like increased sales and user engagement by more accurately estimating the causal effect of recommendations.
This paper addresses the problem of high variance in previous causality-aware recommender systems that use Inverse Propensity Scoring (IPS). The authors propose unifying traditional neighborhood recommendation methods with the matching estimator from causal inference, which avoids the variance issues of IPS. Their experiments show that the proposed methods outperform various baselines in ranking metrics for causal effect.
The business objectives of recommenders, such as increasing sales, are aligned with the causal effect of recommendations. Previous recommenders targeting for the causal effect employ the inverse propensity scoring (IPS) in causal inference. However, IPS is prone to suffer from high variance. The matching estimator is another representative method in causal inference field. It does not use propensity and hence free from the above variance problem. In this work, we unify traditional neighborhood recommendation methods with the matching estimator, and develop robust ranking methods for the causal effect of recommendations. Our experiments demonstrate that the proposed methods outperform various baselines in ranking metrics for the causal effect. The results suggest that the proposed methods can achieve more sales and user engagement than previous recommenders.