Academic Journal of Computing & Information Science, 2022, 5(13); doi: 10.25236/AJCIS.2022.051301.
Yujia Chen1, Siben Li2, Ting Jiang3
1School of Economics and Management, Communication University of China, Beijing, China, 100024
2School of Data Science and Media Intelligence, Communication University of China, Beijing, China, 100024
3School of Computer and Cyber Sciences, Communication University of China, Beijing, China, 100024
Click-through rate (CTR) predictions can impact business revenue and improve user experience. In large-scale enterprise-level advertising systems and recommendation systems, CTR prediction is a very important link, and it has always been one of the key issues of academic research. In the case of extremely unbalanced positive and negative samples, this paper uses as few positive samples as possible to predict the user's advertising click behavior. To solve the above problems, on the basis of data processing, this paper proposes a CTR prediction model that combines feature engineering and machine learning. In terms of feature engineering, this model selects the relevant features of users and videos from the original data based on the feature selection method, selects important features based on random forest (RF), and forms the interaction features of users and videos by factoring interaction parameters. In terms of machine learning, this model is based on the factorization machine (FM) and the integrated learning model of the deep neural network (DNN), the neural factorization machine (NFM) to realize the click rate prediction model. Based on the NFM model, this paper generates a suitable CTR prediction model through the training of the train data set. After continuous optimization and improvement, the parameters with the best prediction effect are selected. The NFM model established in this paper has a high improvement in performance. Compared with FM, DeepFM, FNN, IFM and DCN models, the AUC value of NFM model is improved by about 0.03 on average. The NFM model can better predict the click behavior of users on advertisements, and can provide decision-making reference for advertisement placement, and can be applied to content recommendation of different advertising systems.
Click-through rate prediction; Random Forest; NFM model; AUC
Yujia Chen, Siben Li, Ting Jiang. Research on prediction of user advertising recommendation system based on NFM model. Academic Journal of Computing & Information Science (2022), Vol. 5, Issue 13: 1-6. https://doi.org/10.25236/AJCIS.2022.051301.
 Ma Jia. China Internet Advertising Data Report 2021" published [N]. China Market Monitor, 2022-01-20(004).
 Yan Jinyao, Zhang Hailong, Su Yumin. Research on click-through rate and conversion rate prediction in computational advertising [J]. Journal of Communication University of China (Natural Science Edition), 2021, 28(02):54-60.
 McMahan H B, Holt G, Sculley D, et al. Ad click prediction: a view from the trenches[C]. Proceedings of the 19 th ACM SIGKDD international conference on Knowledge discovery and data mining, 2013:1222-1230.
 Rendle S. Factorization machines[C]. In Proc Int Conf Data Mining, Sydney, NSW, AUS, 2010: 995-1000.
 Covington P, Adams J, Sargin E. Deep neural networks for YouTube recommendations[C]. RecSys′16: Proceedings of the 10th ACM Conference on Recommender Systems, 2016:191-198.