Counterfactual Evaluation of Ads Ranking Models through Domain Adaptation

• URL: http://arxiv.org/abs/2409.19824v1
• Date: Sun, 29 Sep 2024 23:12:04 GMT
• Title: Counterfactual Evaluation of Ads Ranking Models through Domain Adaptation
• Authors: Mohamed A. Radwan, Himaghna Bhattacharjee, Quinn Lanners, Jiasheng Zhang, Serkan Karakulak, Houssam Nassif, Murat Ali Bayir,
• Abstract summary: This approach measures reward for ranking model changes in large-scale Ads recommender systems. Our experiments demonstrate that the proposed technique outperforms both the vanilla IPS method and approaches using non-generalized reward models.
• Score: 4.488611783089895
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a domain-adapted reward model that works alongside an Offline A/B testing system for evaluating ranking models. This approach effectively measures reward for ranking model changes in large-scale Ads recommender systems, where model-free methods like IPS are not feasible. Our experiments demonstrate that the proposed technique outperforms both the vanilla IPS method and approaches using non-generalized reward models.

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