Machine Learning For Bidding Strategies And Click-Through Rate Analysis

Exploratory data analysis

The evolution of machine learning capabilities facilitates the effective bidding process. There are different models are used for analyzing bidding strategies among them lasso, neural networks and ridge are widely used models. From the given data set the calculation was carried out for identifying the click-through rate and cost per click. The pay price approach is used in this model. Based on that the number of clicks analyzed in this model. Finally, the number of clicks is analyzed for the selection of the best bidding strategy.

Machine learning models are used to calculate the bidding strategy. The models such as Ridge, Lasso, and neural networks are analyzed for the bidding strategy. Three different kinds of data sets are identified. CPC, CTR values are calculated with the help of the train data set. The pay price approach is established for this model. Finally, the number of clicks is analyzed for the selection of the best bidding strategy.      

Keywords—Click through rate; Logistic regression; advertisement

Advertisements are used to promote any products. There are different platforms and algorithms are used to generate CTR. Calculate the CPC values is more useful to advertisers. CPC stands for Cost per click. CPC rate is calculated based on number of advertisement clicks. CTR stands for click-through rate. The value of an advertisement is based on impressions, views and queries, etc. Once the users clicked the advertisement which is displayed on the screen, then the click-through rate is generated for the advertiser.

Real-time bidding is such a kind of platform which is for selling the ad impression. This report explains about how RTB is working as server side and also explains how CPC, CTR is calculated. RTB users are widely creates advertisements to impress the customers by giving attractive offers. Those advertisement details is divided into three parts. They are called a test dataset, train dataset and validation dataset. This report also predicts and estimate the value using CPC (cost per click), CPM (cost per impression) and CTR (click through rate).

Exploratory data analysis

  Exploratory data analysis was done based on train dataset. Bidding strategy is helped in maximizing the click-through rate. The number of clicks, impression, pay price, slot dimensions, hours are present in the training data set. First load the dataset into the notebook, after that it was cleaned.

 Variables are present in the train dataset like user id, bid id, user agent, region, city, URL id, slot id, slot width, slot height, slot visible, slot price, bid price. URL id has a null value. Hence it is replaced with the N/A value in order to reject the problems (Bui, Hussain and Kim, 2016). The Bid id column has bid id values. City column has the number of cities. Slot visible has the number of visible slots. Slot price also mentioned in the train data set. The IP address of the system is also mentioned in train dataset.

Slot visibility column has a string and numeric values (Fernandez-Tapia, 2015). CTR, Avg CPM, CPC were calculated using few variables like the number of clicks, pay price, bid price. Exploratory data analysis is done with Jupiter notebook code. This code known as data analysis. IPYNB. In this code, set the common values for advertiser and click. Finally, this code used to easily find out CPC, CTR, CPM, clicks etc…

CTR and Weekdays

 CTR and Weekdays

The CTR stands for Click through rate. It is calculated with the help of one formula. That formula is the number of clicks/number of impressions. In this part of the analysis, Click through rate is calculated for advertisers and plotted against a count the number of variables in the train data set (Sun, Zeng and Xing, 2014). 1458, 3358 values are chosen by advertisers.      

the Click through rate for weekdays calculated for the advertisers 1458 and 3476. That is represented in blue and brown color. Y-axis have the value of the click-through rate. X-axis contains weekdays. CTR value is 0.0012 is the highest value of the third day of the weekdays. The lowest value in click-through rate is 0.0002. 

CTR and Operating System

 Click through rate and the various operating system is used in this analysis process. We take two advertisers for the sample calculation. They are mentioned in blue and brown color. Click through rate is located on the y-axis. The operating system is located on the x-axis. The highest CTR value is 0.012. CTR prediction is done with the help of Data prediction jupyter notebook code. This code known as a CTR prediction code.

CTR and Ad Exchange

The advertiser gives advertisement to the website owners. Website owner calculates the CTR value and ad exchange value. Then construct the graph for that value. We take two advertisers for the sample calculation (Sayedi, 2017).  The advertiser gives two values. There are 1458 and 3358. X-axis consists Ad exchange value. Y-axis consists of click-through value. The below diagram represents the relationship between an exchange and CTR. The ad exchange one column has the null value. The highest CTR value is 0.0009.

CPM

CPM stands for Cost per thousand. It denotes the cost of 1000 advertisement impressions of the single website. CPM formula is calculated based on, cost per click is divided by impressions x 1000.

CPC

 Cost per click is used in giving train data set. Cost per click is calculated based on the given formula: pay price is divided by the number of clicks. CPC has been calculated for six variables. 

CPC Slot visibility

The string and the numeric value is used in slot visibility. A numeric value contains some values like zero, two and etc. it is like a size slot operation (Moretto and Dosi, 2007). The few value of the average CPC is not present in slot visibility. 3358 is the highest value of the slot visibility graph. 

CPC for weekday

 The advertiser 3358 represented in light blue, the eCPC graph has significant average value for CPC. Y-axis consists of average CPC value. X-axis consists Week day’s value. The sixth value represents a relatively higher value in cost per click graph. 

CPC for slot size

 The formula for CPC slot size calculation is slot width *slot height. Numeric values are stored in the variable slot area. Slot width has numeric values and slot height value is also numeric values. CPC for the slot size graph is done with the help of the Data preprocessing code. This code is known as slot size data preprocessing code. 

CTR and Operating System

Bidding

Pay price and bid price are mainly used in the bidding process.  Pay price contains numeric values minimum five to maximum level. Bid price is essential for bidding. Advertiser do bid based on web page impression, views (Mamun, 2015). Real-time bidding uses in advertising department. Bidding operation is happening based on a per-impression basis. Train data set have a bid price and pay the price. Bidding operation is done by bidding the data values jupyter notebook code. So, this code known as bidding the data values code. 

User feedback:

 User give feedback based on cost per thousand impressions, click through rate, CPM (cost per thousand impressions), CPM (Cost per engagement), RPM (revenue per 1000 impressions), cost per click.

1. CTR estimation 

Click-through rate estimation on the basis of logistic regression:

Predict the odds is based on logistic regression. Independent variables are determined click-through rate. Logistic regression calculates the click-through rate based on the logic term CTR. This principle has more webpage view and the results are very clear. The maximum CTR is an ad for slot visibility is five times more than the average click-through rate. An area under Curve is used to predict the click-through rate (Han and Strange, 2013). Pay per impression it doesn’t have ad performance. When the user clicks the advertisement cost of the ad is allotted in the website owner account. Two scenarios are deal with the click-through rate. Straight estimation is not easy and understandable one. Correlations are used to calculate the regression-based CTR.

CTR is calculated based on the given formula.

CTR= Number of clicks / Number of impressions.

Click through rate is naturally going from advertisement term. The logistic regression is used for this CTR estimation.

AUC is accepting the effect of   CTR prediction. The AUC related curve is called ROC. ROC stands for receiver operating characteristics. Medical department uses a ROC curve. This event is related to RTB advertising. The AUC value of the training data set. Train data set has slot width, slot height, slot size. Advertisers are bid the resources based on the estimation of the click-through rate. This estimation is done, on with the help of the ID characteristics, profile characteristics, time characteristics, and numerical characteristics. We use the minimum and maximum normalization process is accepted for the value between zero and one.

1. CTR Estimation-Unbalance problem 

The given data set is huge and imbalanced. We use random data of the train and validation data set as experimental data for practical use. Click records are saved as positive. The other non-click records are saved as negative. Number of non-click on advertisements is known as impressions. Number of clicks in advertisements, are known as Click-NUM. Maintain the good relationship between user’s interest and the basic properties. In this process CTR of the RTB advertisement is involved. In the validation data we have clicked, weekday, hour, bid id, user id, city, slot id, slot price. We use these attributes to predict the CTR of train and validation data set.

Finally, we use temporary properties and user properties like week day, click, slot width, slot height, bid-id. These attributes used an input on the proposed method of the prediction model. It is very important to explore about the number of vanished nodes. We use an ELM algorithm for achieving the accuracy and speed of the train and validation data set. Four variety of functions are provided by ELM algorithm. We use to support vector machines and logistic regression for comparing the two methods. The main objective of this project determines the CTR prediction of the advertisement. We used the real ad dataset to practical experiments by applying measure criteria on the AUC value.

CTR and Ad Exchange

CTR prediction is one of the important unbalance problems in the advertisement department. CTR prediction is computed the CTR with click log. This unbalanced problem uses some strategy, properties, and specifications for finish that problem. Most of the common models are used in the advertisement department. Cost per click billing described as when ad clicked by user, revenue is automatically allocated to the owner of the website. The revenue of the website owner is calculated based on Click through rate and Cost per click.

An input of this model has some set of features is targeted at estimation click-through rate of an ad. It’s a regression problem. We choose logistic regression for solving this problem. it has some accurate probabilities.

Train logistic regression is one of the most commonly used methods for solving the complex problem (Javad Soroor, 2012). We collect the clicks of a month a group of active advertisement in a search ads system. The dataset has a million records about the advertisement. Receiver operating characteristics graphs are very useful for managing their performance. Our train and validation datasets are unbalanced. The main objective of our model improves the accuracy and quality. And also estimate the performance of Area Under roc Curve. In the unit square, AUC is a kind of portion. The value is between 0 and 1.

The below graph represents CTR prediction and compute the revenue. Receiver operating characteristics graphs are useful to manage the properties and estimate the performance of the CTR (Eijk, 2016).

1. SMOTE analysis and CTR estimation 

Table 1 SMOTE and CTR 

The above table represents the comparison table between the CTR and Smote technique. This is the comparison which based on the f1, f2 and f3 values mentioned in the CTR graph. There are two CTR graphs are generated with operating systems and ad exchange. Both the graphs have the same advertisers that is 1458 and 3358. The highest CTR value for the operating system is 0.012 and highest CTR value for Ad exchange is 0.0009. From the analysis both the coordinates for x axis is same but different with the y axis. Hence the CRT rate is also different for the graphs. From the table given the smote results and CTR results are different. So, the smote methodology is used for solving the unbalancing problem. Hence the comparison is done through the advertisement, CPM, clicks etc. 

1. Comparison of SMOTE and CTR

From the above values represents before and after values of SMOTE. The number of active values are increased after the smote analysis. The values are better after the smote process. Hence the values has been affected during the comparison between SMOTE and CTR. The merging and clustering removes the unwanted points and focuses towards the finish of the procedure and lessen the complexity. Because there is no compelling reason to wipe out the most remote created artificial examples after the SMOTE analysis.

After the smote analysis the overfitting for the instances are avoided. So that new synthetic similar instances are created. Later these instances are joined to the original datasets. The random oversampling problems are avoided. There is no loss of the useful data. These are the advantages of smote. Hence the values are better after smote analysis. CTR does not gives the information about the coverage and not does the conversions.

CPM

Findings

  Both CTR and SMOTE are used for different logistic regressions. The CTR estimation is done in the first logistic regression for the measuring the performance using AUC, precision and recall. Those values are inserted in the table. The SMOTE method is used and done for the next logistic regression for solving the unbalancing problem. Those values are also inserted in the table. Both the smote and CTR values are analyzed and compared in the name of before and after of smote. From the comparison smote analysis is the best technique among the two. Because the number of active values are increased during the smote analysis.

1. Methodology to solve the unbalancing problem 

SMOTE-Synthetic Minority Over-sampling Technique

“Smote methodology” is used in the problem for solving the unbalanced problem with logistic regression method. It is used to handle the unbalanced problems using its method. It always provides the “SMOTEd” data set that is used to find the unbalance problem. In this, the purpose of using smote model is to find the resulting model and classification on the newer data sets. From the given dataset, as for our picked metric, the techniques SMOTE with a logistic regression classifier and Balance Cascade give the best execution.

It is widely used method in the oversampling technique. It is proposed to enhance arbitrary oversampling yet its conduct on high-dimensional information has not been completely researched. In this paper we research the properties of SMOTE from a hypothetical and observational perspective, utilizing recreated and genuine high-dimensional information.

Example for smote

SMOTE (click, weekday, hour, bidid, userid, city…..)

The above example represents the actual look of smote technique.

1. Conclusion

      The exploratory analysis was successfully carried out on the given data sets. The different variables like CPC, CTR etc. are analyzed successfully. From the analysis carried out the different key results were identified and it was plotted for better understanding. For achieving the better efficiency the pay price method was used in the model. And the efficiency of pay price strategy is higher than linear bidding strategies. These are the results founded from this analysis.

References

Bui, V., Hussain, A. and Kim, H. (2016). Demand Bidding and Real-Time Pricing-Based Optimal Operation of Multi-Micro grids. International Journal of Smart Home, 10(4), pp.193-208.

Eijk, R. (2016). A Brief Introduction to Real Time Bidding (RTB) (Presentation Slides). SSRN Electronic Journal.

Fernandez-Tapia, J. (2015). Optimal Budget-Pacing for Real-Time Bidding. SSRN Electronic Journal.

Han, L. and Strange, W. (2013). Bidding Wars for Houses. Real Estate Economics, 42(1), pp.1-32.

Javad Soroor, (2012). Smart supplier selection based on voice of customer using an integrated bidding mechanism in real-time. AFRICAN JOURNAL OF BUSINESS MANAGEMENT, 6(29).

Mamun, K. (2015). Combating Shill Bidding in Real Time: Prevention, Detection and Response. Computer and Information Science, 8(2).

Moretto, M. and Dosi, C. (2007). Concession Bidding Rules and Investment Time Flexibility. SSRN Electronic Journal.

Sayedi, A. (2017). Real-Time Bidding in Online Display Advertising. SSRN Electronic Journal.

Sun, L., Zeng, Y. and Xing, H. (2014). Real-Time Bidding Based on MooTools without Refreshing Page. Applied Mechanics and Materials, 496-500, pp.2038-2041.

Order an Essay Now & Get These Features For Free:

Turnitin Report

Formatting

Title Page

Citation

Outline

Place an Order