{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/online-model-evaluation-in-a-large-scale","title":"Online Model Evaluation in a Large-Scale Computational Advertising Platform","arxiv_id":"1508.07678","date":"2015-08-31","proceeding":null,"authors":["Shahriar Shariat","Burkay Orten","Ali Dasdan"],"abstract":"Online media provides opportunities for marketers through which they can\ndeliver effective brand messages to a wide range of audiences. Advertising\ntechnology platforms enable advertisers to reach their target audience by\ndelivering ad impressions to online users in real time. In order to identify\nthe best marketing message for a user and to purchase impressions at the right\nprice, we rely heavily on bid prediction and optimization models. Even though\nthe bid prediction models are well studied in the literature, the equally\nimportant subject of model evaluation is usually overlooked. Effective and\nreliable evaluation of an online bidding model is crucial for making faster\nmodel improvements as well as for utilizing the marketing budgets more\nefficiently. In this paper, we present an experimentation framework for bid\nprediction models where our focus is on the practical aspects of model\nevaluation. Specifically, we outline the unique challenges we encounter in our\nplatform due to a variety of factors such as heterogeneous goal definitions,\nvarying budget requirements across different campaigns, high seasonality and\nthe auction-based environment for inventory purchasing. Then, we introduce\nreturn on investment (ROI) as a unified model performance (i.e., success)\nmetric and explain its merits over more traditional metrics such as\nclick-through rate (CTR) or conversion rate (CVR). Most importantly, we discuss\ncommonly used evaluation and metric summarization approaches in detail and\npropose a more accurate method for online evaluation of new experimental models\nagainst the baseline. Our meta-analysis-based approach addresses various\nshortcomings of other methods and yields statistically robust conclusions that\nallow us to conclude experiments more quickly in a reliable manner. We\ndemonstrate the effectiveness of our evaluation strategy on real campaign data\nthrough some experiments.","url_abs":"http://arxiv.org/abs/1508.07678v1","url_pdf":"http://arxiv.org/pdf/1508.07678v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"online-model-evaluation-in-a-large-scale","repo_url":"https://github.com/turn/ModelEvaluation","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"marketing","task_name":"Marketing"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}