| DC Field | Value | Language |
|---|
| dc.contributor.author | Lyubchyk, Leonid | |
| dc.contributor.author | Grinberg, Galyna | |
| dc.coverage.temporal | 21-25 August 2018, Lviv | |
| dc.date.accessioned | 2020-06-19T12:05:10Z | - |
| dc.date.available | 2020-06-19T12:05:10Z | - |
| dc.date.created | 2018-02-28 | |
| dc.date.issued | 2018-02-28 | |
| dc.identifier.citation | Lyubchyk L. Online Ranking Learning on Clusters / Leonid Lyubchyk, Galyna Grinberg // Data stream mining and processing : proceedings of the IEEE second international conference, 21-25 August 2018, Lviv. — Львів : Lviv Politechnic Publishing House, 2018. — P. 193–197. — (Dynamic Data Mining & Data Stream Mining). | |
| dc.identifier.isbn | © Національний університет „Львівська політехніка“, 2018 | |
| dc.identifier.isbn | © Національний університет „Львівська політехніка“, 2018 | |
| dc.identifier.uri | https://ena.lpnu.ua/handle/ntb/52489 | - |
| dc.description.abstract | Online data stream ranking learning problem is
considered using training data in the form of a sequence of
identical items series, described by a number of features and
relative rank within the series. It is assumed that feature values
and relative ranks of the same items may vary slightly for
different series of observations, and there are stable groups of
items with similar properties. In this regard, the problem of
learning to rank on clusters is stated, while training dataset
consist of estimates of centers of clusters and average rank of
the items inside each cluster. A unified approach to ranking
learning on clusters using kernel models of utility function is
proposed. Recurrent algorithms for estimating the parameters
of a utility function model as well as recurrent ranking
learning algorithm in the space of conjugate variables are developed. | |
| dc.format.extent | 193-197 | |
| dc.language.iso | en | |
| dc.publisher | Lviv Politechnic Publishing House | |
| dc.relation.ispartof | Data stream mining and processing : proceedings of the IEEE second international conference, 2018 | |
| dc.subject | data stream | |
| dc.subject | kernel function | |
| dc.subject | online learning | |
| dc.subject | ranking learning | |
| dc.subject | recurrent estimation | |
| dc.subject | regularization | |
| dc.subject | utility function | |
| dc.title | Online Ranking Learning on Clusters | |
| dc.type | Conference Abstract | |
| dc.rights.holder | © Національний університет “Львівська політехніка”, 2018 | |
| dc.contributor.affiliation | National Technical University “Kharkiv Polytechnic Institute” | |
| dc.format.pages | 5 | |
| dc.identifier.citationen | Lyubchyk L. Online Ranking Learning on Clusters / Leonid Lyubchyk, Galyna Grinberg // Data stream mining and processing : proceedings of the IEEE second international conference, 21-25 August 2018, Lviv. — Lviv Politechnic Publishing House, 2018. — P. 193–197. — (Dynamic Data Mining & Data Stream Mining). | |
| dc.relation.references | [1] . Figueira, S. Greco and M. Ehrgott, Multiple criteria decision analysis: state of the art surveys. Springer, Switzerland, 2005. | |
| dc.relation.references | [2] H. Li, Learning to rank for information retrieval and natural language processing. Morgan & Claypool, Toronto, 2011. | |
| dc.relation.references | [3] T. Y. Liu, “Learning to rank for information retrieval,” Foundations and trends in information retrieval, vol. 3, no. 3, pp.225–331, 2009. | |
| dc.relation.references | [4] L. Hang, “A Short Introduction to Learning to Rank,” IEICE Trans. Information & Systrems, vol. E94-D, no.10, October 2011. | |
| dc.relation.references | [5] J. Furnkranz and E. Hullermeier, “Pairwise preference learning and ranking,” Lecture Notes in Computer Science, vol. 2837, pp. 145-156, 2003. | |
| dc.relation.references | [6] V. Noghin, “Relative importance of criteria: a quantitative approach,” Journal Multi-Criteria Decision Analysis, v. 6, pp. 355-363, 1997. | |
| dc.relation.references | [7] J. Barzilai, “Measurement and preference function modeling”, Intern. Trans. in Operational Research, vol. 12, pp. 173-183, 2005. | |
| dc.relation.references | [8] V. Podvezko, and A. Podviezko, “Dependence of multicriteria evaluation result on choice of preference functions and their parameters,” Technological and Economic Development of Economy. Baltic Jornal of Sustainability,16(1), pp.143–158, 2010. | |
| dc.relation.references | [9] L. Lyubchyk and G. Grinberg, “Preference function reconstruction for multiple criteria decision making dased on machine learning Approach,” Recent developments and new directions in Soft Computing, L.A. Zadeh et al. (Eds), Springer, pp. 53-63, 2014. | |
| dc.relation.references | [10] M. Espinoza, J. Suykens, and B. Moor, “Kernel based partially linear models and nonlinear identification,” IEEE Transactions on Automatic Control, 2005, vol. 50 (10), pp. 1602–1606. | |
| dc.relation.references | [11] J. A. Papini, S. de Amo, A. Kardec and S. Soares, “FPS Mining: A fast algorithm for mining user preferences in data streams,” Journal of Information and Data Management, vol. 5, no. 1, pp. 4–15, February 2014. | |
| dc.relation.references | [12] K. Hofmann, S. Whiteson, and M. de Rijke, “Balancing exploration and exploitation in learning to rank online”, In ECIR 2011: 33-rd European Conference on Information Retrieval. Springer, pp, 251-263, April 2011. | |
| dc.relation.references | [13] K. Hofmann, A. Schuth, S. Whiteson, and M. de Rijke, “Reusing historical interaction data for faster online learning to rank for information retrieval”, In WSDM 2013: International Conference on Web Search and Data Mining, ACM, 2013. | |
| dc.relation.references | [14] L. Lyubchyk and G. Grinberg, “Real time recursive preference learning to rank from data stream,” 1-th IEEE International Conference on Data Stream Mining & Processing, Lviv, Ukraine, pp. 280=285, 2016. | |
| dc.relation.references | [15] V. Strijov, and V. Shakin, “Index construction: the expert-statistical method,” Environmental research, engineering and management, no. 4 (26), pp.51-55, 2003. | |
| dc.relation.references | [16] L. Lyubchyk, V. Kolbasin and R. Shafeev, “Nonlinear signal reconstruction based on recursive moving window kernel method,” 8- th International Conference on Intelligent Data Acquisition and Advanced Computing Systems (IDAACS’2015), Warsaw, Poland, pp. 298-302, 2015. | |
| dc.relation.referencesen | [1] . Figueira, S. Greco and M. Ehrgott, Multiple criteria decision analysis: state of the art surveys. Springer, Switzerland, 2005. | |
| dc.relation.referencesen | [2] H. Li, Learning to rank for information retrieval and natural language processing. Morgan & Claypool, Toronto, 2011. | |
| dc.relation.referencesen | [3] T. Y. Liu, "Learning to rank for information retrieval," Foundations and trends in information retrieval, vol. 3, no. 3, pp.225–331, 2009. | |
| dc.relation.referencesen | [4] L. Hang, "A Short Introduction to Learning to Rank," IEICE Trans. Information & Systrems, vol. E94-D, no.10, October 2011. | |
| dc.relation.referencesen | [5] J. Furnkranz and E. Hullermeier, "Pairwise preference learning and ranking," Lecture Notes in Computer Science, vol. 2837, pp. 145-156, 2003. | |
| dc.relation.referencesen | [6] V. Noghin, "Relative importance of criteria: a quantitative approach," Journal Multi-Criteria Decision Analysis, v. 6, pp. 355-363, 1997. | |
| dc.relation.referencesen | [7] J. Barzilai, "Measurement and preference function modeling", Intern. Trans. in Operational Research, vol. 12, pp. 173-183, 2005. | |
| dc.relation.referencesen | [8] V. Podvezko, and A. Podviezko, "Dependence of multicriteria evaluation result on choice of preference functions and their parameters," Technological and Economic Development of Economy. Baltic Jornal of Sustainability,16(1), pp.143–158, 2010. | |
| dc.relation.referencesen | [9] L. Lyubchyk and G. Grinberg, "Preference function reconstruction for multiple criteria decision making dased on machine learning Approach," Recent developments and new directions in Soft Computing, L.A. Zadeh et al. (Eds), Springer, pp. 53-63, 2014. | |
| dc.relation.referencesen | [10] M. Espinoza, J. Suykens, and B. Moor, "Kernel based partially linear models and nonlinear identification," IEEE Transactions on Automatic Control, 2005, vol. 50 (10), pp. 1602–1606. | |
| dc.relation.referencesen | [11] J. A. Papini, S. de Amo, A. Kardec and S. Soares, "FPS Mining: A fast algorithm for mining user preferences in data streams," Journal of Information and Data Management, vol. 5, no. 1, pp. 4–15, February 2014. | |
| dc.relation.referencesen | [12] K. Hofmann, S. Whiteson, and M. de Rijke, "Balancing exploration and exploitation in learning to rank online", In ECIR 2011: 33-rd European Conference on Information Retrieval. Springer, pp, 251-263, April 2011. | |
| dc.relation.referencesen | [13] K. Hofmann, A. Schuth, S. Whiteson, and M. de Rijke, "Reusing historical interaction data for faster online learning to rank for information retrieval", In WSDM 2013: International Conference on Web Search and Data Mining, ACM, 2013. | |
| dc.relation.referencesen | [14] L. Lyubchyk and G. Grinberg, "Real time recursive preference learning to rank from data stream," 1-th IEEE International Conference on Data Stream Mining & Processing, Lviv, Ukraine, pp. 280=285, 2016. | |
| dc.relation.referencesen | [15] V. Strijov, and V. Shakin, "Index construction: the expert-statistical method," Environmental research, engineering and management, no. 4 (26), pp.51-55, 2003. | |
| dc.relation.referencesen | [16] L. Lyubchyk, V. Kolbasin and R. Shafeev, "Nonlinear signal reconstruction based on recursive moving window kernel method," 8- th International Conference on Intelligent Data Acquisition and Advanced Computing Systems (IDAACS’2015), Warsaw, Poland, pp. 298-302, 2015. | |
| dc.citation.conference | IEEE second international conference "Data stream mining and processing" | |
| dc.citation.spage | 193 | |
| dc.citation.epage | 197 | |
| dc.coverage.placename | Львів | |
| Appears in Collections: | Data stream mining and processing : proceedings of the IEEE second international conference
|
