Counterfactual Evaluation of Ads Ranking Models through Domain Adaptation
<|reference_start|>Counterfactual Evaluation of Ads Ranking Models through Domain Adaptation: 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.<|reference_end|>
@article{radwan2024counterfactual,
    title={Counterfactual Evaluation of Ads Ranking Models through Domain
  Adaptation},
    author={Mohamed A. Radwan, Himaghna Bhattacharjee, Quinn Lanners, Jiasheng
  Zhang, Serkan Karakulak, Houssam Nassif, Murat Ali Bayir},
    journal={arXiv preprint arXiv:2409.19824},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.19824},
    primaryClass={cs.IR cs.AI}
}