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

Efficient Click-Through Rate Prediction for Developing Countries via Tabular Learning