Aug 11, 2020 · This paper proposes an unbiased learning framework for the causal effect of recommendation. Based on the inverse propensity scoring technique, ...
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Sep 22, 2020 · This paper proposes an unbiased learning framework for the causal effect of recommendation. Based on the inverse propensity scoring technique, ...
In this paper, we propose an unbiased learning method for the causal effect of recommendations. We first define the ranking metrics for the causal effect by ...
An unbiased learning framework for the causal effect of recommendation is proposed based on the inverse propensity scoring technique and it is demonstrated ...
Specifically, it first imagines a counterfactual world without these features along specific paths and then compares the factual and counterfactual worlds to ...
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Sep 22, 2020 · ACM RecSys is the premier international forum for the presentation of new research results, systems and techniques in recommender systems.
Feb 15, 2022 · We study the problem of optimizing ranking metrics with unbiased and robust causal estimation for recommender systems.
Feb 25, 2022 · In this paper, we study the problem of learning true causal effect from logged feedbacks under a confounding bias scenario, where recommendation ...
Unbiased learning for the causal effect of recommendation. In Fourteenth ACM ... A Model-Agnostic Causal Learning Framework for Recommendation using Search Data.
Feb 8, 2024 · This survey provides a systematic review of up-to-date papers in this area from a causal theory standpoint and traces the evolutionary development of RS ...