Television Rating Control in the Multichannel Environment Using Trend Fuzzy Knowledge Bases and Monitoring Results

Olexiy Azarov, Leonid Krupelnitsky and Hanna Rakytyanska
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Olexiy Azarov: Computer Facilities Department, Vinnytsia National Technical University, 95, Khmelnitske sh., 21021 Vinnytsia, Ukraine
Leonid Krupelnitsky: Computer Facilities Department, Vinnytsia National Technical University, 95, Khmelnitske sh., 21021 Vinnytsia, Ukraine
Hanna Rakytyanska: Soft Ware Design Department, Vinnytsia National Technical University, 95, Khmelnitske sh., 21021 Vinnytsia, Ukraine

Data, 2018, vol. 3, issue 4, 1-21

Abstract: The purpose of this study is to control the ratio of programs of different genres when forming the broadcast grid in order to increase and maintain the rating of a channel. In the multichannel environment, television rating controls consist of selecting content, the ratings of which are completely restored after advertising. The hybrid approach to rule set refinement based on fuzzy relational calculus simplifies the process of expert recommendation systems construction. By analogy with the problem of the inverted pendulum control, the managerial actions aim to retain the balance between the fuzzy demand and supply. The increase or decrease trends of the demand and supply are described by primary fuzzy relations. The rule-based solutions of fuzzy relational equations connect significance measures of the primary fuzzy terms. Program set refinement by solving fuzzy relational equations allows avoiding procedures of content-based selective filtering. The solution set generation corresponds to the granulation of television time, where each solution represents the time slot and the granulated rating of the content. In automated media planning, generation of the weekly TV program in the form of the granular solution provides the decrease of time needed for the programming of the channel broadcast grid.

Keywords: TV channel rating; expert recommendation systems; inverted pendulum control; fuzzy classification knowledge bases; solving fuzzy relational equations (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2018
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