| Popis: |
One of the critical technologies in the study of search advertising recognition is feature engineering. Most search advertising strategies currently in use are chosen based on past information, which is too subjective to be widely adopted. A feature processing approach based on the preliminary analysis of a user and store data is proposed, and the conversion rate is then forecasted using XGBoost (eXtreme Gradient Boosting). This research uses the advertisements of Ali Search advertising as the research object. Experiments demonstrate that the suggested strategy can greatly enhance the prediction outcomes compared to other prior Feature Engineering. In the quickly expanding field of search advertising, it is essential to recognize search adverts accurately. In this study, we describe a feature engineering strategy for identifying search advertisements. We suggest a collection of custom characteristics considering search adverts' linguistic, structural, and visual elements. We test our method using a sizable dataset of search adverts, and the results show that our features surpass current state-of-the-art methods. Our findings show how crucial feature engineering is to enhance the efficacy of machine learning-based techniques for recognizing search advertisements. |
