Despite the rapid growth of online advertisement in developing countries,
existing highly over-parameterized Click-Through Rate (CTR) prediction models
are difficult to be deployed due to the limited computing resources. In this
paper, by bridging the relationship between CTR prediction task and tabular
learning, we present that tabular learning models are more efficient and
effective in CTR prediction than over-parameterized CTR prediction models.
Extensive experiments on eight public CTR prediction datasets show that tabular
learning models outperform twelve state-of-the-art CTR prediction models.
Furthermore, compared to over-parameterized CTR prediction models, tabular
learning models can be fast trained without expensive computing resources
including high-performance GPUs. Finally, through an A/B test on an actual
online application, we show that tabular learning models improve not only
offline performance but also the CTR of real users.

本研究旨在通过建立点击率预测任务与表格学习之间的联系，证明表格学习模型在 CTR 预测中比超参数化 CTR 预测模型更高效、更有效，并通过 8 个公共 CTR 预测数据集上的实验以及实际在线应用的 A / B 测试，证明这种模型不仅可以提高离线性能，还可以提高真实用户的 CTR。