Causality-Aware Neighborhood Methods for Recommender Systems

• URL: http://arxiv.org/abs/2012.09442v2
• Date: Sat, 30 Jan 2021 05:57:52 GMT
• Title: Causality-Aware Neighborhood Methods for Recommender Systems
• Authors: Masahiro Sato, Sho Takemori, Janmajay Singh, Qian Zhang
• Abstract summary: Business objectives of recommenders, such as increasing sales, are aligned with the causal effect of recommendations. Previous recommenders employ the inverse propensity scoring (IPS) in causal inference. We develop robust ranking methods for the causal effect of recommendations.
• Score: 3.0919302844782717
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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