An improved advertising CTR prediction approach based on the fuzzy deep neural network

Jiang, Zilong; Gao, Shu; Li, Mingjiang

doi:10.1371/journal.pone.0190831

Cited by 21 publications

(9 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Fuzzy system has different characteristics. Fuzzy system can summarize and express corresponding knowledge based on human experience, which is more convenient to understand [ 12 – 14 ]. In fuzzy system, knowledge is stored in the rule set, and the number of rules can be controlled and adjusted, which is less than the calculation of neural network [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Analysis of Sports Injury Estimation Model Based on Mutation Fuzzy Neural Network

Wang

Yang

2021

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

In recent years, with the gradual development of sports, the competition between athletes is becoming more and more fierce. The long training time and heavy body load of athletes lead to the increase of the incidence of sports injury, and the evaluation and analysis of athletes’ sports injury need a lot of manpower and material resources. In order to improve the calculation efficiency of sports injury estimation results and save the cost of estimation and analysis, we propose a sports injury estimation model based on the algorithm model of mutation fuzzy neural network. The sports injury model constructed in this paper can not only systematically evaluate and analyze the degree of sports injury of athletes, but also improve the accuracy and efficiency; at the same time, it has universality for the evaluation and analysis of the degree of sports injury. The construction of this model provides the theoretical basis of big data algorithm for the prevention of sports injury and the application of mutation fuzzy neural network in the field of sports.

show abstract

Section: Introductionmentioning

confidence: 99%

[Retracted] Analysis of Sports Injury Estimation Model Based on Mutation Fuzzy Neural Network

Wang

Yang

2021

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…In the real production environment, an advertising network forecasting application does require the prediction results within milliseconds or even microseconds [37]. Therefore, the approach to the short interval prediction using Neural Network is not just scientifically important but also practically required since it does take great advantages in fast forecasting.…”

Section: Resultsmentioning

confidence: 99%

A LSTM-Hawkes hybrid model for posterior click distribution forecast in the advertising network environment

Hwang

Joe

2020

PLoS ONE

View full text Add to dashboard Cite

In the field of advertising technology, it is a key task to forecast posterior click distribution since 66% of advertising transactions depend on cost per click model. However, due to the General Data Protection Regulation, machine learning techniques to forecast posterior click distribution based on the sequences of an identified user's actions are restricted in European countries. To overcome this barrier, we introduce a contextual behavior concept for the advertising network environment and propose a new hybrid model, which we call the Long Short Term Memory-Hawkes model by combining a stochastic-based generative model and a machine learning-based predictive model. Also, to meet the computational efficiency for the heavy demand in mobile advertisement market, we define gradient exponential kernel with just three hyper parameters to minimize residuals. We have carefully tested our proposed model with production data and found that the LSTM-Hawkes model reduces the Mean Squared Error by at least 27.1% and up to 83.8% on average in comparison to the existing Hawkes Process based algorithm, Hawkes Intensity Process, as well as 39.77% on average in comparison to Multivariate Linear Regression. We have also found that our proposed model improves the forecast accuracy by about 21.2% on average.

show abstract

“…A high CTR means the targeting is effective. Jiang et al (2018) explain a high CTR means that the marketing campaign is contributing to more leads and sales. On the other hand, a low CTR means the ads are not appropriate for the audience.…”

Section: Methodsmentioning

confidence: 99%

CTR Prediction with Deep Neural Networks

Radif¹,

Al-Rammahi²

2020

J. Eng. Appl. Sci.

View full text Add to dashboard Cite

Ad effectiveness on online platforms including Facebook and Google is a challenge for businesses. Since, ad networks use algorithms, ad effectiveness as measured by CTRs is not well understood by marketing and sales executives. CTR prediction with deep neural networks can improve ad CTRs. The AI solution for ad CTRs is useful across industry sectors. In the solution, RNN Models learn representations of sequences by maintaining internal states which are updated sequentially and used as proxy for target prediction. Evidence from research shows that deep neural networks could help businesses enhance CTRs. Problem description:Ads have become cost-intensive for businesses across industry sectors. Since, businesses operate in competitive environments, it is critical for the executive management to focus on good decision-making. Regardless of the business sector, it is critical to enhance the decision-making process as this could mean a significant reduction in ad spending. Machine learning and AI could enhance the ad effectiveness decision-making. Smith (2018) argues that several factors should be considered while analysing whether Click-Through Rate (CTR) is good. Since, achieving higher CTRs is challenging to accomplish (Cheng et al., 2012), machine learning and AI could be deployed for improving CTR. Markus (2017) explains how Google and Facebook use CTRs to determine the quality of ads. Each ad is given a quality score which is based on the CTR. Ads use bidding systems which consider the quality score for ad placement. A high-quality score could outrank the competition and do away the need for outbidding the competition. This means the ad shows higher up in the user's feed at a lower cost-per-click. Thus, the challenge for advertising is to increase the CTRs. Strong CTRs could be achieved by good targeting and creativity. Several factors come into play to make a good CTR. Ad positioning, imagery quality and keywords are the main factors that impact CTR. Though these guidelines are useful there is no clear solution for determining what is a good CTR.Literature review: This section has a review of relevant literature on ad effectiveness, the most significant problem for online ad spending. The discussion shows the relevance of the metric CTR and how AI could solve this problem for businesses based on evidence from academia and industry experts.

show abstract

An improved advertising CTR prediction approach based on the fuzzy deep neural network

Cited by 21 publications

References 33 publications

[Retracted] Analysis of Sports Injury Estimation Model Based on Mutation Fuzzy Neural Network

[Retracted] Analysis of Sports Injury Estimation Model Based on Mutation Fuzzy Neural Network

A LSTM-Hawkes hybrid model for posterior click distribution forecast in the advertising network environment

CTR Prediction with Deep Neural Networks

Contact Info

Product

Resources

About