Ads supply personalization aims to balance the revenue and user engagement, two long-term objectives in social media ads, by tailoring the ad quantity and density. In the industry-scale system, the challenge for ads s...
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ISBN:
(纸本)9798400704369
Ads supply personalization aims to balance the revenue and user engagement, two long-term objectives in social media ads, by tailoring the ad quantity and density. In the industry-scale system, the challenge for ads supply lies in modeling the counterfactual effects of a conservative supply treatment (e.g., a small density change) over an extended duration. In this paper, we present a streamlined framework for personalized ad supply. This framework optimally utilizes information from data collection policies through the doubly robust learning. Consequently, it significantly improves the accuracy of long-term treatment effect estimates. Additionally, its low-complexity design not only results in computational cost savings compared to existing methods, but also makes it scalable for billion-scale applications. Through both offline experiments and online production tests, the framework consistently demonstrated significant improvements in top-line business metrics over months. The framework has been fully deployed to live traffic in one of the world's largest social media platforms.
Post-click conversion rate (CVR) prediction is an essential task for discovering user interests and increasing platform revenues in a range of industrial applications. One of the most challenging problems of this task...
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ISBN:
(纸本)9781450393850
Post-click conversion rate (CVR) prediction is an essential task for discovering user interests and increasing platform revenues in a range of industrial applications. One of the most challenging problems of this task is the existence of severe selection bias caused by the inherent self-selection behavior of users and the item selection process of systems. Currently, doublyrobust (DR) learning approaches achieve the state-of-the-art performance for debiasing CVR prediction. However, in this paper, by theoretically analyzing the bias, variance and generalization bounds of DR methods, we find that existing DR approaches may have poor generalization caused by inaccurate estimation of propensity scores and imputation errors, which often occur in practice. Motivated by such analysis, we propose a generalized learning framework that not only unifies existing DR methods, but also provides a valuable opportunity to develop a series of new debiasing techniques to accommodate different application scenarios. Based on the framework, we propose two new DR methods, namely DR-BIAS and DR-MSE. DR-BIAS directly controls the bias of DR loss, while DR-MSE balances the bias and variance flexibly, which achieves better generalization performance. In addition, we propose a novel tri-level joint learning optimization method for DR-MSE in CVR prediction, and an efficient training algorithm correspondingly. We conduct extensive experiments on both real-world and semi-synthetic datasets, which validate the effectiveness of our proposed methods.
Accurate estimating causal effects of crashes on highway traffic is crucial for mitigating the negative impacts of crashes. Previous studies have built up a series of methods via traditional causal inference theory an...
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Accurate estimating causal effects of crashes on highway traffic is crucial for mitigating the negative impacts of crashes. Previous studies have built up a series of methods via traditional causal inference theory and machine learning methods to estimate the impacts of crashes. Since the structures and variable dimensions of traditional causal inference models are pre-defined, they can not accommodate the characteristics of individual crashes. They only can estimate the average causal effects for the crashes in certain categories, e.g., crash types, crash severity, and occurring locations. For machine learning-based algorithms, they cannot be used for causal reasoning due to their reliance on correlation rather than causation. However, considering the impacts of crashes on traffic status vary across influential factors, such as time periods and locations, heterogeneous causal effects are essential for a better understanding of the effects on traffic status and crash intervention strategy development. To address the aforementioned issues, this study proposes a novel doublyrobust causal machine learning framework to infer heterogeneous treatment effects of crashes on highway traffic status. doubly robust learning (DRL), integrating machine learning techniques to perform predictive tasks, is applied into the framework due to its stronger robustness. Considerning treatment predictors and colliders may bring bias in estimation results, Conditional Shapley Value Index (CSVI) is proposed for selecting confounders from numerous factors. A 3-year crah dataset collected by 3594 real highway crashes in Washington is utilized for demonstrating the designed experiments, including construting confidence intervals, estimated errors evaluation, and sensitivity analysis of variable selection for various thresholds of CSVI. According to the results, the distinctive propagation and dissipation processes of congestion caused by various types of crashes can be achieved. The results al
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