Unbiased Learning for the Causal Effect of Recommendation

Abstract

Increasing users' positive interactions, such as purchases or clicks, is an important objective of recommender systems. Recommenders typically aim to select items that users will interact with. If the recommended items are purchased, an increase in sales is expected. However, the items could have been purchased even without recommendation. Thus, we want to recommend items that results in purchases caused by recommendation. This can be formulated as a ranking problem in terms of the causal effect. Despite its importance, this problem has not been well explored in the related research. It is challenging because the ground truth of causal effect is unobservable, and estimating the causal effect is prone to the bias arising from currently deployed recommenders. This paper proposes an unbiased learning framework for the causal effect of recommendation. Based on the inverse propensity scoring technique, the proposed framework first constructs unbiased estimators for ranking metrics. Then, it conducts empirical risk minimization on the estimators with propensity capping, which reduces variance under finite training samples. Based on the framework, we develop an unbiased learning method for the causal effect extension of a ranking metric. We theoretically analyze the unbiasedness of the proposed method and empirically demonstrate that the proposed method outperforms other biased learning methods in various settings.

arXiv commentaccepted at RecSys 2020, updated several experiments

References in corpus (5)

• Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering
• Causal Embeddings for Recommendation
• Measuring the Business Value of Recommender Systems
• Unbiased Learning to Rank with Unbiased Propensity Estimation
• Estimating the Causal Impact of Recommendation Systems from Observational Data

Cited by in corpus (6)

• Causal Collaborative Filtering
• Dynamic Causal Collaborative Filtering
• The Importance of Causality in Decision Making: A Perspective on Recommender Systems
• Treatment Effect Estimation for User Interest Exploration on Recommender Systems
• Automatic Feature Fairness in Recommendation via Adversaries
• Uncertainty Calibration for Counterfactual Propensity Estimation in Recommendation