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- Title
Feature embedding in click-through rate prediction.
- Authors
Pahor, Samo; Kopič, Davorin; Demšar, Jure
- Abstract
We tackle the challenge of feature embedding for the purposes of improving the click-through rate prediction process. We select three models: logistic regression, factorization machines and deep factorization machines, as our baselines and propose five different feature embedding modules: embedding scaling, FM embedding, embedding encoding, NN embedding and the embedding reweighting module. The embedding modules act as a way to improve baseline model feature embeddings and are trained alongside the rest of the model parameters in an end-to-end manner. Each module is individually added to a baseline model to obtain a new augmented model. We test the predictive performance of our augmented models on a publicly accessible dataset used for benchmarking click-through rate prediction models. Our results show that several proposed embedding modules provide an important increase in predictive performance without a drastic increase in training time.
- Subjects
FORECASTING; LOGISTIC regression analysis; PREDICTION models; REAL-time bidding (Internet advertising); PREDICTIVE tests
- Publication
Electrotechnical Review / Elektrotehniski Vestnik, 2023, Vol 90, Issue 3, p75
- ISSN
0013-5852
- Publication type
Article