The Approach to Creating the Recommendation System of Piano Pieces

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dc.citation.epage104
dc.citation.spage102
dc.contributor.affiliationNational Technical University "Kharkiv Polytechnic Institute"
dc.contributor.authorHolshtein, Maiia
dc.contributor.authorBabkova, Nadiia
dc.coverage.placenameЛьвів ; Харків
dc.coverage.placenameLviv ; Kharkiv
dc.coverage.temporal22-23 April 2021, Kharkiv
dc.date.accessioned2022-05-23T10:50:13Z
dc.date.available2022-05-23T10:50:13Z
dc.date.created2021-05-04
dc.date.issued2021-05-04
dc.description.abstractNowadays a lot of descriptions of pieces of musical art can be found in Internet or in specialized collections. There is no recommendation system that offers certain composition for performance according to its difficulty level. This paper suggests the approach to creating the recommendation system of piano pieces. The approach is based on checking for collocations in descriptions of each composition. This paper shows the statistical method PMI used for searching the collocations indicating on certain difficulty level. In addition it also discusses the main problems during creating own recommendation system.
dc.format.extent102-104
dc.format.pages3
dc.identifier.citationHolshtein M. The Approach to Creating the Recommendation System of Piano Pieces / Maiia Holshtein, Nadiia Babkova // Computational linguistics and intelligent systems, 22-23 April 2021, Kharkiv. — Lviv ; Kharkiv, 2021. — Vol Vol. II : Proceedings of the 5th International conference, COLINS 2021, Workshop, Kharkiv, Ukraine, April 22-23. — P. 102–104.
dc.identifier.citationenHolshtein M. The Approach to Creating the Recommendation System of Piano Pieces / Maiia Holshtein, Nadiia Babkova // Computational linguistics and intelligent systems, 22-23 April 2021, Kharkiv. — Lviv ; Kharkiv, 2021. — Vol Vol. II : Proceedings of the 5th International conference, COLINS 2021, Workshop, Kharkiv, Ukraine, April 22-23. — P. 102–104.
dc.identifier.issn2523-4013
dc.identifier.urihttps://ena.lpnu.ua/handle/ntb/56809
dc.language.isoen
dc.relation.ispartofComputational linguistics and intelligent systems, 2021
dc.relation.references[1] An Easy Introduction to Machine Learning Recommendation Systems, 2021. URL: https://www.kdnuggets.com/2019/09/machine-learning-recommender-systems.html.
dc.relation.references[2] Yak-vyrvatysia-z-informatsiinoi-bulbashky, 2020. URL: https://wz.lviv.ua/blogs/388693-yakvyrvatysia-z-informatsiinoi-bulbashky
dc.relation.references[3] Linden G., Smith B., York J. Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Computing, 2003. 7 (1), 76–80. doi: https://doi.org/10.1109/mic.2003.1167344
dc.relation.references[4] Meleshko Y. V. Problemy recomendatsiynykh system ta metody yikh rishennia, Systemy upravlinnia, navigatsii ta zvyazku. 4, 2018: 120 – 124. doi: https://doi.org/10.26906/SUNZ.2018.4.120
dc.relation.references[5] V.V. Lytvyn, V. A. Vysotska, V. V. Shatskykh, I. V. Kogut, O. S. Petruchenko, L.V. Dziubyk, V. V. Bobrivets, V. M. Panasiuk, S. I. Sachenko and M. P. Komar. Design of a recommendation system based on collaborative filtering and machine learning considering personal needs of the user. Eastern-European Journal of Enterprise Technologies, 2019. 4(2 (100), 6–28. https://doi.org/10.15587/1729-4061.2019.175507
dc.relation.references[6] Collocation discovery with PMI, 2020. URL: https://py.plainenglish.io/collocation-discoverywith-pmi-3bde8f351833
dc.relation.referencesen[1] An Easy Introduction to Machine Learning Recommendation Systems, 2021. URL: https://www.kdnuggets.com/2019/09/machine-learning-recommender-systems.html.
dc.relation.referencesen[2] Yak-vyrvatysia-z-informatsiinoi-bulbashky, 2020. URL: https://wz.lviv.ua/blogs/388693-yakvyrvatysia-z-informatsiinoi-bulbashky
dc.relation.referencesen[3] Linden G., Smith B., York J. Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Computing, 2003. 7 (1), 76–80. doi: https://doi.org/10.1109/mic.2003.1167344
dc.relation.referencesen[4] Meleshko Y. V. Problemy recomendatsiynykh system ta metody yikh rishennia, Systemy upravlinnia, navigatsii ta zvyazku. 4, 2018: 120 – 124. doi: https://doi.org/10.26906/SUNZ.2018.4.120
dc.relation.referencesen[5] V.V. Lytvyn, V. A. Vysotska, V. V. Shatskykh, I. V. Kogut, O. S. Petruchenko, L.V. Dziubyk, V. V. Bobrivets, V. M. Panasiuk, S. I. Sachenko and M. P. Komar. Design of a recommendation system based on collaborative filtering and machine learning considering personal needs of the user. Eastern-European Journal of Enterprise Technologies, 2019. 4(2 (100), 6–28. https://doi.org/10.15587/1729-4061.2019.175507
dc.relation.referencesen[6] Collocation discovery with PMI, 2020. URL: https://py.plainenglish.io/collocation-discoverywith-pmi-3bde8f351833
dc.relation.urihttps://www.kdnuggets.com/2019/09/machine-learning-recommender-systems.html
dc.relation.urihttps://wz.lviv.ua/blogs/388693-yakvyrvatysia-z-informatsiinoi-bulbashky
dc.relation.urihttps://doi.org/10.1109/mic.2003.1167344
dc.relation.urihttps://doi.org/10.26906/SUNZ.2018.4.120
dc.relation.urihttps://doi.org/10.15587/1729-4061.2019.175507
dc.relation.urihttps://py.plainenglish.io/collocation-discoverywith-pmi-3bde8f351833
dc.rights.holdercopyrighted by its editors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
dc.rights.holder© 2021 Copyright for the individual papers by the papers’ authors. Copying permitted only for private and academic purposes. This volume is published and
dc.subjectRecommendation systems
dc.subjectmachine learning
dc.subjectPMI
dc.subjectcollocations
dc.subjectmusical art
dc.subjectpiano pieces
dc.subjectclassification
dc.titleThe Approach to Creating the Recommendation System of Piano Pieces
dc.typeArticle

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