https://oldena.lpnu.ua/handle/ntb/52526| Title: | A Fuzzy Model of Television Rating Control with Trend Rules Tuning based on Monitoring Results |
| Authors: | Azarov, Olexiy Krupelnitsky, Leonid Rakytyanska, Hanna |
| Affiliation: | Vinnytsia National Technical University |
| Bibliographic description (Ukraine): | Azarov O. A Fuzzy Model of Television Rating Control with Trend Rules Tuning based on Monitoring Results / Olexiy Azarov, Leonid Krupelnitsky, Hanna Rakytyanska // Data stream mining and processing : proceedings of the IEEE second international conference, 21-25 August 2018, Lviv. — Львів : Lviv Politechnic Publishing House, 2018. — P. 369–374. — (Hybrid Systems of Computational Intelligence). |
| Bibliographic description (International): | Azarov O. A Fuzzy Model of Television Rating Control with Trend Rules Tuning based on Monitoring Results / Olexiy Azarov, Leonid Krupelnitsky, Hanna Rakytyanska // Data stream mining and processing : proceedings of the IEEE second international conference, 21-25 August 2018, Lviv. — Lviv Politechnic Publishing House, 2018. — P. 369–374. — (Hybrid Systems of Computational Intelligence). |
| Is part of: | Data stream mining and processing : proceedings of the IEEE second international conference, 2018 |
| Conference/Event: | IEEE second international conference "Data stream mining and processing" |
| Issue Date: | 28-Feb-2018 |
| Publisher: | Lviv Politechnic Publishing House |
| Place of the edition/event: | Львів |
| Temporal Coverage: | 21-25 August 2018, Lviv |
| Keywords: | TV channel rating expert recommendation systems fuzzy resources control fuzzy classification knowledge bases solving fuzzy relational equations |
| Number of pages: | 6 |
| Page range: | 369-374 |
| Start page: | 369 |
| End page: | 374 |
| Abstract: | The problem of constructing the recommendation rules for the television rating control is considered. A hybrid approach combining the benefits of semantic training and fuzzy relational equations in simplification of the process of expert recommendation systems construction is proposed. The problem of retaining the television rating can be attributed to the problems of fuzzy resources control. The trends of demand-supply relationships are described by the primary fuzzy knowledge bases. Rules refinement by solving the primary system of fuzzy relational equations allows avoiding labor-intensive procedures for the generation and selection of expert rules. |
| URI: | https://ena.lpnu.ua/handle/ntb/52526 |
| ISBN: | © Національний університет „Львівська політехніка“, 2018 © Національний університет „Львівська політехніка“, 2018 |
| Copyright owner: | © Національний університет “Львівська політехніка”, 2018 |
| URL for reference material: | http://inter.ua/ru/about/rating http://ot.vntu.edu.ua/katalog |
| References (Ukraine): | [1] S. Žilic Fišer, Successful Television Management: the Hybrid Approach. Peter Lang GmbH: Int. Verlag der Wissenschaften, 2015. [2] D. Schuurman, L. D. Marez, P. Veevaete, and T. Evens, “Content and context for mobile television: integrating trial, expert and user findings,” Telematics and Informatics, vol. 26 (3), pp. 293–305, August 2009. [3] D. Véras, T. Prota, A. Bispo, R. Prudêncio, and C. Ferraz, “A literature review of recommender systems in the television domain,” Expert Systems with Applications, vol. 42 (22), pp. 9046–9076, December 2015. [4] J. Oh, S. Kim, J. Kim, and H. Yu, “When to recommend: a new issue on TV show recommendation,” Information Sciences, vol. 280, pp. 261–274, October 2014. [5] F. Fraile and J. C. Guerri, “Simple models of the content duration and the popularity of television content,” Journal of Network and Computer Applications, vol. 40, pp. 12–20, April 2014. [6] Y. Zhang, W. Chen, and Z. Yin, “Collaborative filtering with social regularization for TV program recommendation,” Knowledge-Based Systems, vol. 54, pp. 310–317, December 2013. [7] Y. Xu and J. Yin, “Collaborative recommendation with user generated content,” Eng. Appl. Artif. Intell., vol. 45, pp. 281–294, October 2015. [8] Z. Wang and L. He, “User identification for enhancing IPTV recommendation,” Knowledge-Based Systems, vol. 98, pp. 68–75, April 2016. [9] L. Boratto, S. Carta, and G. Fenu, “Investigating the role of the rating prediction task in granularity-based group recommender systems and big data scenarios,” Information Sciences, vol. 378, pp. 424–443, February 2017. [10] E. Panova, A. Raikov, and O. Smirnova, “Cognitive television viewer rating,” Procedia Comput. Sci., vol. 66, pp. 328–335, 2015. [11] J.-S. Lin, Y. Sung, and K.-J. Chen, “Social television: examining the antecedents and consequences of connected TV viewing,” Computers in Human Behaviour, vol. 58, pp. 171–178, May 2016. [12] A. Rotshtein and H. Rakytyanska, Fuzzy Evidence in Identification, Forecasting and Diagnosis. Studies in Fuzziness and Soft Computing, vol. 275, Heidelberg: Springer, 2012. [13] A. Rotshtein and A. Rakityanskaya, “Inventory control as an identification problem based on fuzzy logic,” Cybernetics and Systems Analysis, vol. 42 (3), pp. 411–419, May 2006. [14] A. Rotshtein and H. Rakytyanska, “Expert rules refinement by solving fuzzy relational equations,” In Proc. of the VIth IEEE Conference on Human System Interaction. 6-8 June, 2013, Sopot, Poland, pp. 257–264, 2013. [15] A. Rotshtein and H. Rakytyanska, “Optimal design of rule-based systems by solving fuzzy relational equations,” In: S. Hippe Z., L. Kulikowski J., Mroczek T., Wtorek J. (eds) Issues and Challenges in Artificial Intelligence. Studies in Computational Intelligence, vol. 559, pp. 167–178, Springer, 2014. [16] H. Rakytyanska, “Fuzzy classification knowledge base construction based on trend rules and inverse inference,” Eastern-European Journal of Enterprise Technologies, vol. 1(3), pp. 25–32, 2015. [17] H. Rakytyanska, “Classification rule hierarchical tuning with linguistic modification based on solving fuzzy relational equations,” Eastern-European Journal of Enterprise Technologies, vol. 1(4), pp. 50–58, 2018. [18] A. Rotshtein and H. Rakytyanska, “Adaptive diagnostic system based on fuzzy relations,” Cybernetics and Systems Analysis, vol. 45(4), pp. 623–637, July 2009. [19] A. Rotshtein and H. Rakytyanska, “Fuzzy logic and the least squares method in diagnosis problem solving,” In: Sarma R. (Ed.). Genetic diagnoses. New York: Nova Science Publishers, pp. 53–97, 2011. [20] http://inter.ua/ru/about/rating. [21] O. D. Azarov, L. V. Krupelnitsky, V. Y. Steiskal, and O. A. Bilokon’, “Specialized and Measuring Equipment of Own Design and Production for TV and Radio Broadcasting,” Catalog of the Scientific and Technical Center «Analog-Digital Systems», Vinnitsya: VNTU, 2015. http://ot.vntu.edu.ua/katalog. |
| References (International): | [1] S. Žilic Fišer, Successful Television Management: the Hybrid Approach. Peter Lang GmbH: Int. Verlag der Wissenschaften, 2015. [2] D. Schuurman, L. D. Marez, P. Veevaete, and T. Evens, "Content and context for mobile television: integrating trial, expert and user findings," Telematics and Informatics, vol. 26 (3), pp. 293–305, August 2009. [3] D. Véras, T. Prota, A. Bispo, R. Prudêncio, and C. Ferraz, "A literature review of recommender systems in the television domain," Expert Systems with Applications, vol. 42 (22), pp. 9046–9076, December 2015. [4] J. Oh, S. Kim, J. Kim, and H. Yu, "When to recommend: a new issue on TV show recommendation," Information Sciences, vol. 280, pp. 261–274, October 2014. [5] F. Fraile and J. C. Guerri, "Simple models of the content duration and the popularity of television content," Journal of Network and Computer Applications, vol. 40, pp. 12–20, April 2014. [6] Y. Zhang, W. Chen, and Z. Yin, "Collaborative filtering with social regularization for TV program recommendation," Knowledge-Based Systems, vol. 54, pp. 310–317, December 2013. [7] Y. Xu and J. Yin, "Collaborative recommendation with user generated content," Eng. Appl. Artif. Intell., vol. 45, pp. 281–294, October 2015. [8] Z. Wang and L. He, "User identification for enhancing IPTV recommendation," Knowledge-Based Systems, vol. 98, pp. 68–75, April 2016. [9] L. Boratto, S. Carta, and G. Fenu, "Investigating the role of the rating prediction task in granularity-based group recommender systems and big data scenarios," Information Sciences, vol. 378, pp. 424–443, February 2017. [10] E. Panova, A. Raikov, and O. Smirnova, "Cognitive television viewer rating," Procedia Comput. Sci., vol. 66, pp. 328–335, 2015. [11] J.-S. Lin, Y. Sung, and K.-J. Chen, "Social television: examining the antecedents and consequences of connected TV viewing," Computers in Human Behaviour, vol. 58, pp. 171–178, May 2016. [12] A. Rotshtein and H. Rakytyanska, Fuzzy Evidence in Identification, Forecasting and Diagnosis. Studies in Fuzziness and Soft Computing, vol. 275, Heidelberg: Springer, 2012. [13] A. Rotshtein and A. Rakityanskaya, "Inventory control as an identification problem based on fuzzy logic," Cybernetics and Systems Analysis, vol. 42 (3), pp. 411–419, May 2006. [14] A. Rotshtein and H. Rakytyanska, "Expert rules refinement by solving fuzzy relational equations," In Proc. of the VIth IEEE Conference on Human System Interaction. 6-8 June, 2013, Sopot, Poland, pp. 257–264, 2013. [15] A. Rotshtein and H. Rakytyanska, "Optimal design of rule-based systems by solving fuzzy relational equations," In: S. Hippe Z., L. Kulikowski J., Mroczek T., Wtorek J. (eds) Issues and Challenges in Artificial Intelligence. Studies in Computational Intelligence, vol. 559, pp. 167–178, Springer, 2014. [16] H. Rakytyanska, "Fuzzy classification knowledge base construction based on trend rules and inverse inference," Eastern-European Journal of Enterprise Technologies, vol. 1(3), pp. 25–32, 2015. [17] H. Rakytyanska, "Classification rule hierarchical tuning with linguistic modification based on solving fuzzy relational equations," Eastern-European Journal of Enterprise Technologies, vol. 1(4), pp. 50–58, 2018. [18] A. Rotshtein and H. Rakytyanska, "Adaptive diagnostic system based on fuzzy relations," Cybernetics and Systems Analysis, vol. 45(4), pp. 623–637, July 2009. [19] A. Rotshtein and H. Rakytyanska, "Fuzzy logic and the least squares method in diagnosis problem solving," In: Sarma R. (Ed.). Genetic diagnoses. New York: Nova Science Publishers, pp. 53–97, 2011. [20] http://inter.ua/ru/about/rating. [21] O. D. Azarov, L. V. Krupelnitsky, V. Y. Steiskal, and O. A. Bilokon’, "Specialized and Measuring Equipment of Own Design and Production for TV and Radio Broadcasting," Catalog of the Scientific and Technical Center "Analog-Digital Systems", Vinnitsya: VNTU, 2015. http://ot.vntu.edu.ua/katalog. |
| Content type: | Conference Abstract |
| Appears in Collections: | Data stream mining and processing : proceedings of the IEEE second international conference |
| File | Description | Size | Format | |
|---|---|---|---|---|
| 2018_Azarov_O-A_Fuzzy_Model_of_Television_369-374.pdf | 366.18 kB | Adobe PDF | View/Open | |
| 2018_Azarov_O-A_Fuzzy_Model_of_Television_369-374__COVER.png | 609.88 kB | image/png | View/Open |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.