2021/04/15 by Joonyoung Yi, Yi, Joonyoung, Buru Chang +1
Computer Science · Decision Sciences · #Advanced Bandit Algorithms Research #Caching and Content Delivery #FOS: Computer and information sciences #Human-Computer Interaction (cs.HC) #Machine Learning (cs.LG) #Recommender Systems and Techniques #cs.HC #cs.LG
paper · pdf · doi:10.48550/arxiv.2104.07553
ICLR 2021 Workshop (PML4DC), 8 pages, 2 figures
arxiv created 2021/04/15 · openalex publication_date 2021/04/15 · arxiv updated 2021/04/16 · openalex created_date 2021/04/26 · openalex updated_date 2026/07/28
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.