<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The manufacturing industry requires a unique recommendation system to suggest products and raw materials, but its performance is often poor in massive data environment. In order to solve the similarity connection problem of large-scale real-time data, the optimised incremental similarity connection method which is used to deal with streaming data can be used to concisely obtain the longest common additive sequence of two given input sequences. This paper, on the basis of the recursion equation, applies a very simple linear space algorithm to solve this problem and adopts new states to carry out similarity connection of incremental data. The experimental results demonstrate that this method can not only ensure the accuracy of real-time recommendation system but also greatly reduce the computed amount.</p>
</abstract>

The manufacturing industry requires a unique recommendation system to suggest products and raw materials, but its performance is often poor in massive data environment. In order to solve the similarity connection problem of large-scale real-time data, the optimised incremental similarity connection method which is used to deal with streaming data can be used to concisely obtain the longest common additive sequence of two given input sequences. This paper, on the basis of the recursion equation, applies a very simple linear space algorithm to solve this problem and adopts new states to carry out similarity connection of incremental data. The experimental results demonstrate that this method can not only ensure the accuracy of real-time recommendation system but also greatly reduce the computed amount.

A long command subsequence algorithm for manufacturing industry recommendation systems with similarity connection technology

Fujian Provincial Key Laboratory of Data Intensive Computing

Key Laboratory of Intelligent Computing and Information Processing

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

The manufacturing industry requires a unique recommendation system to suggest products and raw materials, but its performance is often poor in massive data...

A long command subsequence algorithm for manufacturing industry recommendation systems with similarity connection technology

Online veröffentlicht: 30. Sept. 2022

Seitenbereich: 789 - 798

Eingereicht: 20. Mai 2022

Akzeptiert: 16. Aug. 2022

DOI: https://doi.org/10.2478/amns.2021.2.00232

Schlüsselwörter
Long command subsequence, Similarity connection, Recommendation system, Real-time, Manufacturing industry, Massive data, Memory computation

© 2023 Siyu Huang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

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A long command subsequence algorithm for manufacturing industry recommendation systems with similarity connection technology

Online veröffentlicht: 30. Sept. 2022

Seitenbereich: 789 - 798

Eingereicht: 20. Mai 2022

Akzeptiert: 16. Aug. 2022

DOI: https://doi.org/10.2478/amns.2021.2.00232

SchlüsselwörterLong command subsequence, Similarity connection, Recommendation system, Real-time, Manufacturing industry, Massive data, Memory computation

© 2023 Siyu Huang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Schlüsselwörter
Long command subsequence, Similarity connection, Recommendation system, Real-time, Manufacturing industry, Massive data, Memory computation