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Efficient Click-Through Rate Prediction for Developing Countries via Tabular Learning
[article]
2021
arXiv
pre-print
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
arXiv:2104.07553v1
fatcat:6fj6bo5minc2hhpny5kuz6etie