The article critically analyzes four methods of evaluating the rating of commercial content of the Internet service, namely: "Just plus"; "Plus / Minus"; "Star"; "Combined". The basis of the development of the information service model is the "Star" method, since it provides a detailed evaluation with a fixed scale of values. The mathematical support of the "Star" method for calculating the content rating has been constructed and optimized. The basis of the method is the formula of the arithmetic average, weighted, taking into account the average number of votes and the average rating of all electronic documents. In order to influence the rating, the control parameter k is entered. Two uncontrolled parameters are added: the number of sales and downloads that affect the actual content rating. In order to get rid of the rating dependence between the different categories and because the rating value went beyond the 5-star rating, the rating was scaled for the content, taking into account the maximum rating in the relevant category in the 5-star interval. The relationship between content rating and merchant profits and service has been introduced. The task of calculating a commission for the sale of content, depending on its rating, is to motivate sellers to increase some content, thus the service will increase the rating of this content. The purpose of content rating is to increase the number of sales by constructing plausible rating lists. Software for ehuub.com project was developed The web - service quickly and simply selling and creating
commercial content. Examples of ranking and software demonstration are given. Further research will focus on the development and improvement of the intellectual component, which will reduce the subjective impact on the value of the controlling parameter k.
1. Berko, A. Yu., Vysotskaya, V. A., & Pasichnik, V.V. (2009). Systems of electronic content-commerce: monograph. Lviv: Lviv Polytechnic Publishing House.
2. Vysotskaya, V. A., & Chyrun, L. V. (2014). The architecture of electronic content-commerce systems. Bulletin of the National University "Lviv Polytechnic". Series: "Information Systems and Networks", № 783, 39-55.
3. Vysotskaya, V. A. (2008). Algorithms and means of working out of information resources in systems of electronic content-commerce. Bulletin of the National University "Lviv Polytechnic". Series: "Information Systems and Networks", № 621. 78-96.
4. Clifton, B. (2009). Google Analytics: professional website traffic analysis. M .: Williams.
5. Basyuk, Т., Vasyluk, А. (2016). Factors for ranking online resources by the Google search engine. Bulletin of the National University "Lviv Polytechnic". Series: "Information Systems and Networks", № 854. 3-10.
6. Basyuk, T. (2018). The Popularization Problem of Websites and Analysis of Competitors. In: Shakhovska N., Stepashko V. (eds). Advances in Intelligent Systems and Computing II. CSIT 2017. Advances in Intelligent Systems and Computing, vol 689. Springer, Cham pp. 54-65.
7. Burov, Y., Zavuschak, І. (2017). Methods of processing the context in intelligent systems. Bulletin of the National University "Lviv Polytechnic". Series: "Information Systems and Networks", № 872, 121-131.
8. Berko, A. Yu., Kis, Ya. P., & Sukhoversky, V. I. (2011). System of content monitoring of news Internet resources. Bulletin of the National University "Lviv Polytechnic". Series: "Information Systems and Networks", № 715, 13-21.
9. Ashmanov, I., Ivanov, А. (2011). Optimization and promotion of sites in search engines. St. Petersburg: Peter.
10. Troelsen, A. (2015). The programming language C # 5.0 and the .NET 4.5. М.: Williams.
11. Schildt, H. (2011). C# 4.0 The Complete Reference. М.: Williams.
12. Lerman, J. (2010). Programming Entity Framework. Second Edition. "O'Reilly Media, Inc.".
14. Lerner, A. (2013). Ng-book - The Complete Book on AngularJS. Fullstack. io.