| Issue |
E3S Web of Conf.
Volume 458, 2023
International Scientific Conference Energy Management of Municipal Facilities and Environmental Technologies (EMMFT-2023)
|
|
|---|---|---|
| Article Number | 09017 | |
| Number of page(s) | 6 | |
| Section | IT and Mathematical Modeling in Energy Systems | |
| DOI | https://doi.org/10.1051/e3sconf/202345809017 | |
| Published online | 07 December 2023 | |
Topo-heuristic reconfiguration of algebraic LSTM components to optimize temporal modulation in long-range dependencies
T.F. Gorbachev Kuzbass State Technical University, 650000, Kemerovo, 28 Vesennya st., Russian Federation
* Corresponding author: pylovpa@kuzstu.ru
In this paper, the authors present a modified LambdaRank ranking algorithm based on the mathematical apparatus of the basic machine learning model (LSTM). LambdaRank is an effective method for ranking objects according to their importance, so it is often used as a mandatory component in search engines and recommendation systems. In this paper, an improvement of the algorithm is proposed by using optimisation techniques and introducing additional parameters for more accurate and stable ranking. The effectiveness of the proposed approach is verified on experimental real application data. The obtained accuracy results of the upgraded algorithm have also been analysed and compared with the classical variation of LambdaRank.
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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