  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.
