Applicability analysis of prediction methods in the system for selection personalized offers by analytical modeling

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Abstract


The relevance of the work is justified by the frequent occurrence of the need to solve the problems of choosing personalized offers in information systems and the many possible methods of machine learning, among which it is necessary to choose the most suitable one. The purpose of this study is to simulate a system for selecting personalized offers as a queuing system for estimating equipment costs when using each of the methods necessary to service the required part of requests for a given time limit. This solves the problem of assessing the minimum number of servicing devices (backend servers) required to ensure the operation of the system at a given level. The paper shows that the system can be described by a multichannel queuing system without losses. The distribution function of the spent time of the request in the system (the service time plus the waiting time in the queue) is calculated, since in the literature for such systems only the distribution function of the waiting time in the queue is described. Transformations of the expression for the probability of waiting are given, which solve the overflow problem in the software implementation. In the final part, as an example, the system was modeled according to the given parameters, and the minimum number of servicing devices was estimated to ensure a given system response time. Based on the data obtained, it is possible to make a decision on the advisability of using one or another method for predicting the frequency of user clicks or ranking.


About the authors

Yuriy S. Fedorenko

Bauman Moscow State Technical University (National Research University of Technology)

Author for correspondence.
Email: fedyura11235@mail.ru
SPIN-code: 1755-4017
5 2-ya Baumanskayа St, bldg 1, Moscow, 105005, Russian Federation

Degree Seeker at the Department of Informatics and Control Systems, Faculty of Computer Science and Management Systems

References

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  4. Xinran H, Junfeng P, Ou J, Tianbing X, Bo L, Tao H et al. Practical Lessons from Predicting Clicks on Ads at Facebook. In: Proceedings of the Eighth International Workshop on Data Mining for Online Advertising. New York, USA: ACM Publ.; 2014. р. 1––9. http://dx.doi.org/10.1145/2648584.2648589

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Copyright (c) 2021 Fedorenko Y.S.

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