Extreme Learning Machines for Fast Training of Click-Through Rate Prediction Models
This work addresses the need for faster training in CTR prediction for online advertising, but it is incremental as it adapts an existing method (ELMs) to a new domain with minor enhancements.
The authors tackled the problem of slow training for Click-Through Rate (CTR) prediction models by applying Extreme Learning Machines (ELMs) enhanced with embedding layers, achieving competitive F1 results on Avazu and Criteo datasets while significantly reducing training time compared to state-of-the-art models like Masknet.
Extreme Learning Machines (ELM) provide a fast alternative to traditional gradient-based learning in neural networks, offering rapid training and robust generalization capabilities. Its theoretical basis shows its universal approximation capability. We explore the application of ELMs for the task of Click-Through Rate (CTR) prediction, which is largely unexplored by ELMs due to the high dimensionality of the problem. We introduce an ELM-based model enhanced with embedding layers to improve the performance on CTR tasks, which is a novel addition to the field. Experimental results on benchmark datasets, including Avazu and Criteo, demonstrate that our proposed ELM with embeddings achieves competitive F1 results while significantly reducing training time compared to state-of-the-art models such as Masknet. Our findings show that ELMs can be useful for CTR prediction, especially when fast training is needed.