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Applied Marketing Analytics (AMA)2023Brands usually invest in a portfolio of digital ad products for brand consideration and conversion, and their performance is commonly evaluated with ad - attributed metrics. However, these metrics limit the measurement of advertising effectiveness within a short time window, typically of two weeks. Therefore, they could underestimate the total effect if some ad products' efficacy lasts beyond the measurement
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KDD 2023 Workshop on Multi-Armed Bandits and Reinforcement Learning (MARBLE), ICML 2023 Workshop on The Many Facets of Preference-based Learning2023Motivated by bid recommendation in online ad auctions, this paper considers a general class of multi-level and multi-agent games, with two major characteristics: one is a large number of anonymous agents, and the other is the intricate interplay between competition and cooperation. To model such complex systems, we propose a novel and tractable bi-objective optimization formulation with mean-field approximation
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KDD 2023 Workshop on Artificial Intelligence for Computational Advertising (AdKDD)2023This paper proposes a learning model of online ad auctions that allows for the following four key realistic characteristics of contemporary online auctions: (1) ad slots can have different values and click-through rates depending on users’ search queries, (2) the number and identity of competing advertisers are unobserved and change with each auction, (3) advertisers only receive partial, aggregated feedback
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KDD 2023 ACM SIGKDD Workshop on Causal Discovery, Prediction and Decision (CDPD)2023Companies offering web services routinely run randomized online experiments to estimate the “causal impact” associated with the adoption of new features and policies on key performance metrics of interest. These experiments are used to estimate a variety of effects: the increase in click rate due to the repositioning of a banner, the impact on subscription rate as a consequence of a discount or special
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ECML PKDD 20232023Causal Impact (CI) measurement is broadly used across the industry to inform both short- and long-term investment decisions of various types. In this paper, we apply the double machine learning (DML) methodology to estimate average and conditional average treatment effects across 100s of customer action types for e-commerce and digital businesses and 100s of millions of customers that can be used in decisions
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