https://oldena.lpnu.ua/handle/ntb/56809| Title: | The Approach to Creating the Recommendation System of Piano Pieces |
| Authors: | Holshtein, Maiia Babkova, Nadiia |
| Affiliation: | National Technical University "Kharkiv Polytechnic Institute" |
| Bibliographic description (Ukraine): | Holshtein 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. |
| Bibliographic description (International): | Holshtein 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. |
| Is part of: | Computational linguistics and intelligent systems, 2021 |
| Issue Date: | 4-May-2021 |
| Place of the edition/event: | Львів ; Харків Lviv ; Kharkiv |
| Temporal Coverage: | 22-23 April 2021, Kharkiv |
| Keywords: | Recommendation systems machine learning PMI collocations musical art piano pieces classification |
| Number of pages: | 3 |
| Page range: | 102-104 |
| Start page: | 102 |
| End page: | 104 |
| Abstract: | Nowadays 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. |
| URI: | https://ena.lpnu.ua/handle/ntb/56809 |
| ISSN: | 2523-4013 |
| Copyright owner: | copyrighted by its editors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). © 2021 Copyright for the individual papers by the papers’ authors. Copying permitted only for private and academic purposes. This volume is published and |
| URL for reference material: | https://www.kdnuggets.com/2019/09/machine-learning-recommender-systems.html https://wz.lviv.ua/blogs/388693-yakvyrvatysia-z-informatsiinoi-bulbashky https://doi.org/10.1109/mic.2003.1167344 https://doi.org/10.26906/SUNZ.2018.4.120 https://doi.org/10.15587/1729-4061.2019.175507 https://py.plainenglish.io/collocation-discoverywith-pmi-3bde8f351833 |
| References (Ukraine): | [1] An Easy Introduction to Machine Learning Recommendation Systems, 2021. URL: https://www.kdnuggets.com/2019/09/machine-learning-recommender-systems.html. [2] Yak-vyrvatysia-z-informatsiinoi-bulbashky, 2020. URL: https://wz.lviv.ua/blogs/388693-yakvyrvatysia-z-informatsiinoi-bulbashky [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 [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 [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 [6] Collocation discovery with PMI, 2020. URL: https://py.plainenglish.io/collocation-discoverywith-pmi-3bde8f351833 |
| References (International): | [1] An Easy Introduction to Machine Learning Recommendation Systems, 2021. URL: https://www.kdnuggets.com/2019/09/machine-learning-recommender-systems.html. [2] Yak-vyrvatysia-z-informatsiinoi-bulbashky, 2020. URL: https://wz.lviv.ua/blogs/388693-yakvyrvatysia-z-informatsiinoi-bulbashky [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 [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 [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 [6] Collocation discovery with PMI, 2020. URL: https://py.plainenglish.io/collocation-discoverywith-pmi-3bde8f351833 |
| Content type: | Article |
| Appears in Collections: | Computational linguistics and intelligent systems. – 2021 р. |
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