Nowadays, important mechanisms of study are content and techniques of its creation, the problem of influencing the target audience, which itself seeks to shape communication processes. Internet content occupies a position of powerful communication technology, which continues to grow rapidly and gain influence. Creating a large number of advertisements, especially texts, is extremely expensive. Therefore, it is worth considering how generate these texts automatically. In this regard, it is possible to assume that the development of a method of forming the context of advertising and target audience based on learning associative rules is relevant and can increase the effectiveness of advertising, and thus reduce the cost of online advertising of higher education institutions. The input data used a survey of students majoring in Computer Science, regarding admission. The 152 students took part in the survey and answered 10 questions. The experimental results confirmed, the proposed method enabled to increase the effectiveness of advertising on social networks at least in 23%, and reduce the price in 90%.
Given the complexity of the modern digital advertising ecosystem, there are many studies describing the impact of advertising content on social networks on attracting customers [19], using data from Facebook in: medicine [20], psychology [21], sociology [22], politics [23] and others. Research [24, 25] provides an understanding of social networks for higher education institutions such as Instagram,
In [
The aim of the article [
In [17] the study analyzed the various needs of customers, focusing on online advertising based on
methods of classification, segmentation and clustering. In [16] the model of selection of the optimal
amount of advertising on various Internet resources is analyzed in order to achieve the desired coverage
of the target audience. In addition, the method of multi-criteria optimization with the definition of the
obtained objective function is considered, which allows considering simultaneously the various aspects
of media selection issue and optimal budgeting and budget allocation. The proposed approach [
In a study [
Article [18] proposes the generation of advertising texts based on keywords that take into account
product information. Ins [
The above-mentioned works mostly analyze the actions of users on online advertising. There are also a number of works that present research on methods of associative rules learning. There are also works that use different approaches to the formation of content and target audience of Internet advertising (analogues).
Therefore, goal of this paper is to develop a method of forming the context of advertising and the target audience based on the associative rules learning.
The novelty of the work is the formation of the most profitable (in the economic aspect) text of the Free Economic Zone advertising and the corresponding target group, which will increase the effectiveness of the advertising campaign based on learning the rules of association.
To reduce the time spent on the formation of advertising content for the target audience, the authors have developed a method of forming the context of advertising and the target audience based on the associative rules learning. The proposed method is illustrated schematically (Fig. 1) and is represented by the following steps:
Step 1. Conduct a survey of students (Block 1). It is important to determine the gender characteristics of the respondents, as this will make it possible to determine the target audience in the future. Convert to csv format (Block 2).
2
3 5
pairs of elements 5.2
5.3
5.4 5.5
6
context of advertising and target audience Figure 1: Algorithmic structure of forming the context of advertising and target audience based on learning associative rules
Step 2. Calculation of support for each individual element (Block 3). Support is simply the number of transactions during which a particular product (or combination of products) occurs.
Step 4.2. Formation of rules (Block 5.3). The most frequent sets of elements, converted into association rules, in the format: Element X => Element Y.
Step 4.3. Calculation of confidence (Block 5.4). Confidence shows the percentage of cases in which this rule applies.
Step 4.3. Elevator calculation (Block 5.5). Raising a rule is an indicator of effectiveness, which indicates the strength of the relationship between the products in the rule. Raising the rule is determined by the following formula: where, P is the probability of the frequency of combination of elements in the generated rule.
The Python language was chosen to conduct the method of forming the context of advertising and the target audience based on th associative rules learning.
The input data used a survey of students majoring in Computer Science, regarding admission. 152 students took part in the survey and answered 10 questions. All students’ feedbacks are in .csv format. The sample is representative, as the survey was conducted among students majoring in Computer Science, who themselves have recently been faced with the choice of where to enter, so their feedback is the most informative for this type of advertising.
Before starting the analysis of the rules of the association, first determine the frequency distribution of the elements (Table 2). The chart shows that the majority of male respondents, as well as the highest number of answers, said that they found information on their own in social networks, majoring in
Count 67 59 41 39 37 35 33 32 30 29 29 29 20 17 15 12 11 11 10 8 6 5 4 4 4 2 2
Item name Internet of Things. Robotics. Software control of drones Quality presentation of the material A friend studied in the same specialty A friend recommended Web I wanted to study to be a programmer Because a large variety of specialties could not be determined Robotics. Software control of drones Chose occasionally I like working with computers and I am interested in the specialty Prospect My chosen specialty is the most relevant Because this specialty covers many areas that I really like Many acquaintances study in Ternopil A relative studied at the university I have wanted to become a programmer for a long time, so I chose this specialty I found information from social networks on my own I really wanted to enroll in FCIT and the most advanced training is on CS I was interested in computer science
In addition, it is important to note that even the most frequent response of more than 11% is male (Table 2). Next, we will use this information as a guide when setting a minimum support threshold. Table 2 The most common answers
Index 13 40 47 45 37 46 34 39 31 41 43
Item augmented reality passed here for public training interest in technology interest in design interested in the presentation of the specialty by representatives interest in computers female advised familiar relatives saw a lot of interesting information on social networks independently found information male Count 29 29 30 32 34 35 37 39 41 59 67
After converting the dataset into the desired list, we will display the results, namely the rules, which in the future will allow to form the context of advertising and target groups.
Therefore, after starting the algorithm was generated:
48 candidates were found for sets of length 1; Found 15 large sets of items of length 1.
105 candidates were found for sets of length 2; 32 large sets of items of length 2 were found.
Based on the experimentally significant parameters of the algorithm, the generated rules are filtered.
Almost all of the received rules mention the gender of the respondent (male), which indicates the main target audience for the specialty “Computer Science”. Rules that are more than two elements of the answer, allow to create content for advertising. Let's form some examples of content (Table 3).
To compare the effectiveness of the generated advertising content based on the associative rules
learning, a comparative experiment was conducted on Facebook on the business page “Computer
Science of ZUNU”. The first option (Option 0) advertising (Fig. 2), developed on the basis of the rules
identified in previous research, namely:
The greatest interaction with the video advertising of Facebook “Computer Science ZUNU”
had males in the age category 18-25, 35-55 [
Fig. 3 presents an advertisement formed on the basis of learning associative rules, a comparative experiment was conducted on Facebook on the business page “Computer Science of ZUNU”. The content used is from Table 3 and the target audience is male for all age groups.
Figure 3: New variants of advertising "Computer Science" of the Western Ukrainian National University on Facebook
Table 4 presents a comparison of the effectiveness of the generated advertising content based on the associative rules learning, in the period from May 4, 2021 – May 31, 2021 with all content options, including the old.
Table 4 Comparison of the effectiveness of the generated advertising content
Results
Coverage
Readings 2,5 1,5 0,5 2 1 0
Thus, the proposed method enabled to increase the effectiveness of advertising on social networks at least in 23%, and reduce the price in 90%. Moreover, the authors believe that growing the number of student surveys can increase the quality of associative rules learning as well as get better keywords for content formation. That will lead to increasing the effectiveness of advertising and reducing its costs respectively.
Developed method of forming the context of advertising and target audience based on the associative rules learning makes it possible to increase the effectiveness of advertising, and thus reduce the cost of online advertising of higher education institutions. Also, the developed method will allow to form rules between the answers of respondents for the formation of advertising content and the definition of the target group.
To implement the developed method, we used a survey of students majoring in Computer Science, regarding admission. 152 students took part in the survey and answered 10 questions.
The proposed method enabled to increase the effectiveness of advertising on social networks at least in 23%, and reduce the price in 90%. Moreover, the authors believe that growing the number of student surveys can increase the quality of associative rules learning as well as get better keywords for content formation. That will lead to increasing the effectiveness of advertising and reducing its costs respectively.
Unlike analogues [
In the future, the authors plan to develop the information system that will automatically generate the content of the advertising message and select the target audience using the deep learning methods [27, 28].