论文标题
推荐系统的因果感知邻里方法
Causality-Aware Neighborhood Methods for Recommender Systems
论文作者
论文摘要
推荐人的业务目标，例如增加销售额，与建议的因果效应保持一致。针对因果效应的先前推荐人在因果推理中采用反相反倾向评分（IPS）。但是，IPS容易患有较高的差异。匹配估计量是因果推理领域中的另一种代表性方法。它不使用倾向，因此没有上述方差问题。在这项工作中，我们将传统的邻里推荐方法与匹配估计器统一，并为建议的因果效应开发出强大的排名方法。我们的实验表明，所提出的方法在因果效应的排名指标方面的表现优于各种基准。结果表明，所提出的方法比以前的推荐人可以实现更多的销售和用户参与度。
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.
