2 research outputs found
Unbiased Learning for the Causal Effect of Recommendation
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.Comment: accepted at RecSys 2020, updated several experiment